Adquisición_de_datos_de_ChEMBL.ipynb

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)\n",
        "\n",
        "---\n",
        "**Realizado por:** Raziel Cedillo\n",
        "**Contacto:** brayanraziel1997@gmail.com\n",
        "(junio 2023)\n",
        "\n",
        "---\n"
      ],
      "metadata": {
        "id": "wTgebEbOTxYg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **ChEMBL** (*Obtención de datos y ejemplos*)\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "33KdEHEVFTiO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Las siguientes paqueterias y herramientas quimioinformáticas nos ayudan a acceder a los datos de ChEMBL:\n",
        "*   chembl_webresource_client\n",
        "*   pandas\n",
        "\n",
        "\n",
        "Puntos a revisar:\n",
        "1. Moléculas\n",
        "2. Fármacos\n",
        "3. Blancos\n",
        "4. Actividad\n",
        "5. Ensayos\n",
        "6. Tejidos\n",
        "7. Células\n",
        "8. Utilidades\n",
        "9. Ejercicio global.\n",
        "\n",
        "Para realizar una búsqueda sólo necesitamos tres cosas: 1) responder a la pregunta ¿qué quiero conocer?, 2) el **recurso disponible a emplear** y 3) la **palabra clave** que nos permitirá aplicar un filtro, opcionalmente se pueden aplicar filtros adicionales."
      ],
      "metadata": {
        "id": "7KoIE7jvFy7O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "# **Paqueterias**: instalación e importación\n",
        "---\n"
      ],
      "metadata": {
        "id": "hhoitJyAKLfw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.utils import io\n",
        "import tqdm.notebook\n",
        "import os, os.path, sys, random, subprocess\n",
        "total = 100\n",
        "with tqdm.notebook.tqdm(total=total) as pbar:\n",
        "    with io.capture_output() as captured:\n",
        "        pbar.update(20)\n",
        "        !pip install matplotlib, seaborn, pandas\n",
        "        #Librerías gráficas\n",
        "        import matplotlib.pyplot as plt\n",
        "        from IPython.display import SVG\n",
        "        import seaborn as sns\n",
        "        pbar.update(40)\n",
        "        #Librerias de herramientas primarias:\n",
        "        import pandas as pd\n",
        "        %config Completer.use_jedi = False\n",
        "        import json\n",
        "        pbar.update(30)\n",
        "        #Busqueda en ChEMBL (API)\n",
        "        !pip install chembl_webresource_client\n",
        "        from chembl_webresource_client.new_client import new_client #Importar API\n",
        "        from chembl_webresource_client.utils import utils\n",
        "        from tqdm.auto import tqdm\n",
        "        from pathlib import Path\n",
        "        # Montar unidad de Google Drive\n",
        "        from google.colab import drive\n",
        "        drive.mount(\"/content/drive\")\n",
        "        pbar.update(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "5729fbaa46e44eb09fbb1339569b2e18",
            "cdd405edd4b44b479ae0002052299eaf",
            "c970de6c6eee47c7bdcfbf7ac8ff267b",
            "bd636d8cfa4d41b09bbb3e19ab5221dc",
            "b2d338473db04ff0b154bc76949a15d8",
            "d50cdc69fb4f4baa8a9d8ffc60ce2263",
            "fafe3b872af6427bb01e415ade76e8e4",
            "b8cfdeae488144198718e0127b9c32cd",
            "925f32dd29b14d1784734e2170f04255",
            "7ac6d807078344bb9ad2d82642cab365",
            "c9c2b5b1ca584898a63f778ae26b8ffd"
          ]
        },
        "id": "ysNGZbAz0grG",
        "outputId": "e0a187f6-1ef5-4587-f1a4-e7e2ba1a475c"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/100 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5729fbaa46e44eb09fbb1339569b2e18"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "# **Ejemplo y estructura de una búsqueda general.**\n",
        "---\n",
        "\n",
        "\n",
        "En la siguiente figura se ejemplifica visualmente una búsqueda a través de la interfaz gráfica de ChEMBL (lado izquierdo) en comparación con una realizada a través de la API (lado derecho).\n",
        "\n",
        "En la primera sección de ambas metodologías se realiza la búsqueda de la palabra \"aspirina\" (\"aspirin\", subrayado en rosa).\n",
        "En la segunda sección, se realiza la búsqueda de un identificador de ChEMBL asociado a la aspirina (CHEMBL25, subrayado en verde).\n"
      ],
      "metadata": {
        "id": "n0YEPNiCHviL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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1vQ1ZooArbW9DRtb0NWaKAK21vQ0bW9DVmigCKIEE5FS0UUAYmnf8jPqP/Xnbf8Aoc9bdYmnf8jPqP8A1523/oc9bdABRUMV1BPJJHFNG7xnDqpyVPv+RpxniWXyjKgk27tpPOOmaAJKKj86Mf8ALRe3f1OB+tIbmAIzmaPaucndwMUAS0UwyxrII2kUORkKTyR/kilLqDgsoPoTQA6imCVGAIkUgjIOetIk0UgykiMM44PfOP5g0ASUU0uoOCyg/Wk81Mgb1yTgc0APoqMTxFyglTcACRnsen8jT9wwDuHPT3oAWimCVGZlDqWU7SM9DjOPyoMsanBkUEDOCaAH0UzzE/vr69aFkRvuup5xwaAH0UwSxtIYxIpcdVzyKPNTJG9cg4PPTv8A1oAfRTPNjzjzFzyevp/+ugSxnP7xeOvNAD6Karq2drA4OOKaJ4jL5QkTzCCdueeOtAElFRSXEMLbZJUQ7S2GOOB1P6ipFYMMqQRkjigBaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDCf/ka77/rxtv/AEOetW1/j/Csp/8Aka77/rxtv/Q561bX+P8ACgCwelclpdhYX/hh7/UT/pEokkuJy2GicE5APbbjH4V1tZU3h3TJ53leA/vG3yIGIRz6kdKaZnOLZkPq2oSWGo6ja3SC309RtikTmYLGrksf4c54pkmrazLFJdw3EKR/2itmkTRZ+VnC7ifUbv0rdudB067uWnlgyz7d6hiFfHTcO9TnTbQxlPKG0zi4x/00DBs/mBVXRPJPuc82q6qupppIuovMN6YDcNGMlPIEvTpnORT49V1ObVDoouI1nSZw115Y+ZFSNsBem794B+BrQ1Hw7bahfW9wwChZjNLgkFm8vYCD2I4/Kpj4f042yweSRtkMocMd+88E7uuccUXQuSd3r/wxQ0rWLyfWF064aNjGLhZJFGA7I0W0j04kOR6iqljqur6xcyRQXUMCJC0m8RhiSJpUUfTCCtlvD+nGCGIQlBCWKMrEN833snvnvU9lpNlpwxawLGNnl4H93czY/Nm/OldDUJ31ehzkOr6nrWl3N1BPFbLBZxyNGyZ3s8Qc5PZecfgagstY1C2tLHfL5dpHDaqGMe9GBRN29uqnnj8K6GTw3pciRobfCJEsO1WIDIvQH1ApT4c0sup+z4UBAUDEK2wALkd8AD8qd0T7Oe9zVoooqDoCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAxNO/5GfUf+vO2/9DnrbrE07/kZ9R/687b/ANDnrboAo2umi3uWlaVn+Z2jXkbd7Fjnnnk1R1g6P58q6iGB8uPcdx+Ybjt4BzwQTmtymlEY5ZVJ9SKAMGKHQo7FLqFHWEsI1Kh85VsgY68EE1SS38MFS4icRqgwxL9CWPTr1B/Ous2Lt27Rj0xTHiBX5VQHjqueM80Ac1NeeGdSvI7uXe02VCsyOvO4AcY9cfzqzcf2DdC31eZS3nyBI5NrZ3Luxx26GpIrzU/JRpdHHmERgorDjkhjn2ABxUkN1cuw+3afHAioHhO7diTkY/Lv7mgDMZ/DDLCjK4H7rZlJBjqU/mf0pLUeGAYpYI5eJVZWKyABhuYHnsMn8/etCK/1PbCZdEVMlQ580fIO56duTipZb67FxtttKWa33qBMsgGQepx7c/p+ABVVdA1mee72vJIgSaRiHXgBgp/Ld+dJp1roN2sv2KN/kw7ffQ8qR1OOxP51aivdRABbSljztyFfP8JOOg6HA/OpbG6u5riSKbTxbLGqktncHznIHA6YH+FAGXbad4duEmMMDloIo5ZEDNkLtO3vycZ6VIb/AELfZ2Y3k2pRoVAYbGI+XJ+n/wBetpmcWnnQQYfZu8phtJ44X2OcVlSXurKsmzRo2bA2EOMfd78evHagB8cmkX08l7l2ltQtw+S3yZTg46H5R2qib3wzLPPdfP5kil5JPLfps+noelaf2+aKzuZJ7ERyhMxwg5835c7cgevHSmWt3dCRY5dIS2t1ABfeCAu3sAPwoAy5E8MmVpZfNPnLswVcZ+Tce391v5VbsDoMluNRtkZBZqJejfJmMDp3O3jv3q49xdvdLHBYoYGkx5x6BNowcd+SR9BWgvzrKix7Cp25ZeG4HPuOcfgaAMCGbw7/AGmt9EH+1fvJA+x8dMsfTpVzWotLle3bUIXcbX2FSeAcA8Dk5yO1LLdaik7hNMQxgSANu5JA+U9O9LPe6iThNL3D94FctnbhcrkcdTx1oAxo5/DESzR5lKlpSx2uQwOGboOnT8q0m0/SL9mX7M8pdZGADFc42hgOe+4cVNcXl5D5kiaYJEXeQOjHG3GOO/P4gVfjXZMm+FRIys29F4X7uRn34+uPagDPbWdJin3b3L24eHcI2OPmQEdOeStF0dOtZ/naYS7WkG0kYVmyeegyR9asXct5BLthsI7hTzu3bepxjofrVNrvUYvNCaGHwTj9+Du5HqPqfwoAi1DU9AvPMF3I7BFZGwrjA3KD099tWotSsbeUqnmnmRmb+EEy7Tn/AIEfyp93eTruNjp6XQC5Pz7fm3DI6enP4VCLrUxI6tpMbKScNvwMeZgZ4P8AD81AE3/CRacZAglcsQGx5bdCwXPTplhSv4h06OTY8rBvmOBGx4DFSeB6g1At9qjMQdERRjgmUdd3fj8ams7i+uJ/Ln0xLZNm7zC4bndjbj3GTmgC3a6jb3kC3ELHyWO0Ow2/Nu245560t3fwWKB7glIyyqGxnLE4xxzUkeyWEExbVzkK64wQeDj8M1IVDDBAI96AM2LxBp8zqkcrFm24BQrwW29x61GfEmnLJDG0jCSXHy7Sduc9SOO38q1TGhOSik/Sk8qMY+RePagCkms2rhCC2HEZXjrvJA/lTrPVrW+DeQzkrGshBQjAIyP6/kaueVGMYRePaozawFi3lqGJBJHGcdKAM4+JdMVULyyLv24zE38QJHb0B/I1etr6G6hjmjJ8uX/Vsw27uvAB57GpPstv5gk8mPeDkNtGc4x/Lin+Wny/IvynK8dOMcfmaAKt3qUFlNFHcB1ErbUYDIJ/Cqh8R6f5ZZWkYqm8gIf7pbGemcA1rFVb7yg/UUnlpjGxcfSgDMXxHpjF9sznYMt+6bgbd3p6CmL4o0l2CrcOSV3f6pumM56ela3lx/3F/KmrBCmdsSLuO44XqfWgCgNfsDEJfMfYT1KH+7uzj6VdiuUlVGGVEgym7gsMA5x171J5cf8AcX8qXYu4NtG5RgHHQUALRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGE/8AyNd9/wBeNt/6HPWra/x/hWU//I133/Xjbf8Aoc9atr/H+FAFio1nif7siHjPB7VIelYyaCka/LJg5BBHAGM8Y9Oaat1Im5JrlVzXLqASWAAGSc9KTzo8KfMXDHA56ms2LRViSZBIzCWIxHcxOARzSnR189XD4RXV1QdFIp2XcnmqfymiJYyxUOpYdRnpS71/vD86ypNHeS5aQTBVO7AA5BbO7nv1/CkTQxGgVZSTuU5JOcAnI/EHBosu4uep/KagniMxhEimQDJXPIqSsqHQYIdck1RZZC7kt5ZPAJAGfyHStWk7dC4OTvzKwUUUUiwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDE07/kZ9R/687b/wBDnrbrE07/AJGfUf8Arztv/Q5626ACiiigApkuzZ87bRuGDnHORj9cU+myMVTIXdjGRnt3oAwY7C7hVWfxCSgcMxMY+b5s468elTWVtd20ySvqy3n7sptkwg5I+bjOemOnfrWbHJoF66N9jnDqV5kdgRlx/tc8tn8/Q06OTQLjynFncYLqVZ2YYbPGctnr+H5UAXWtLwrF5OtKQDGWG3AZc+uT1AP1pTpl7tiY665TdGTmMYfb1Gc9zUNtDo16BCtrITDEsgBkfaAM4AOcHG4/n7VHHdaSIbWMxTiFZEMMOc+W7F+eOeCG6nHP0oAtRWV0fKca6Wj/AHZAVBhl2+uf4uua0LVHSKFFuVmdVUSuc/OuDggZ4Of61gL/AMI/JLGfsdwZMRMuXYnAViv8XOBnj39au2Wo2Ki3tLeC4jSOVUiVTkHKk8nJyByfwFAGhewrNNCVuzA6SBmGT842tgHke5/4DVP7BcmNU/toj7oJVAMjZj17nLVfmgttRTybhdzR8tGHIKllIwcH0JrBxocd3LbNYypOCobLMVyVK8HOOAxH457ZABoXcV9PbLHBqUMMkaEBxz5h24BI7eveoobG/CTPLrnyAZBCDCjb1P48/hWfa/2BE/yWly0qxJucs3I2gjnIBOPSrdtLpTP9lhSZEuiI2VWLB12A/MTkjhscH1NAEy2c8ygx68XXBVvlBDZj9iMfe3fl9TqwhspslRwP9aeTk7RjHPHY1g3baLJcSwyW1w0kJbJDnAOzaSPmx90f1p9vrumQQ20Ua3YSCMqg2nBCr/F2zjsaANG+thPPmO/8hlWRWXJPzFQAcZHQc496hlsLqXc41kqoL7tqDAyAF78YPPvmqupT6Resst/aSM8IdQN2GA2gnAU85BpLW20q5sLlbcT21kG3T7mwCVAOTuyegHp0oAvTLNdhWXUIoAgZC0Z3bsEZPbBGCO/WiW3urs7Pt6RnymTEfO49N3bB5HH0qrf6No1naGaSwd4Y9xwkjZy2F4Ge/wDSobfUdItrK4FtDPDDKXeQqTvLZUHHOf4vwxQBZuNPvw0u7XTFvLMg8oDaCRx17dPxrTEqJOqieLZtZmUt8xORz16c/qKyr3VdN1CylguYHeEkgpJ8u7bye+R0/l61Xa30A6VdSmzmNtFKY5ELPljuXoM88qv5UAXr+0kld5LbVzaAhUIAD4O8knk9TnFOjtLu3uPtK6issSlgYnG1TlifvZPIyB+FZ7HRIVkY2kybA2/bIQQA+3nDevT2NJ5ujpazxfZ50sx+9aPIw7bsZ67s5xxnjvQBbW0uG2v/AG7lCAVAUYyGBPOeQcEfjTra0uHY7dZE5YK4GzjaH3cYbp/DWVNH4aM2ZNOmZwvBBYgjft4IbHXNblrpllaP5drvtpQp+VZSx2564bI5wKAI7SB4bhftOr/aQyYEZULkhx83B9SB+NakQkEY80qX7lRgVVt9KsrW6+0xRETbCm4ux4LbiOT681aijEUYQMzY7sck0APooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDCf/ka77/rxtv/AEOetW1/j/Csp/8Aka77/rxtv/Q561bX+P8ACgCSViuMHFUINWtbm5a3huA8q5yo7VavZlt4DK4YqoJO0ZNc/bT6LbXLXcTsHc4JOcf57UAaF9qBsxLLLP5cMa7mY9AMVTs9fh1B2S1vPMZRkgelReIJrMJLbXhbypo23beygDJz+IrF0qXQdPu0WzmcvcLhSxJBG7H8z+lAHb/aPLt1klk2rgZYmnRXKzxh4pVdDyCpyKzNQezns/7Pu2YLOgB2989v0qroi6Rpcf2WwlcpK44JJwen4dhQB0G9vU1TXWLR7z7It0pn3FdnfIzx+h/KkudStbOTy55NpwD09c4/lWXENHXUftiSyecHL5z8ufmB/maAOh3t6mje3qaqWt/bXpIgkD461ZoAdvb1NG9vU02igB29vU0b29TTaKAHb29TRvb1NNooAdvb1NG9vU02igB29vU0b29TTaKAHb29TRvb1NNooAdvb1NG9vU02igB29vU0b29TTaKAHb29TRvb1NNooAdvb1NYcNx4g/tEiVYjbeZLjjBK4/d/rwatzxxT3LMLrbtQxsoOMEg/wCP6VVGlyLgjVpQBjHI4HH+FMh2fUiS+8Sqka/YoHO3Lu74wfoPypI9U8SOQW0qFVG4HMvPA4x9TUzaa4BJ1SUIx24yMZJ/nVq6iS5IRLwo6sSNrAkZpolppaMz5dR8T7EKadbg4DH95nnHI/8Ar1u280728TTLslZAXQHIVscisoWmQyDU3PBDDcP89xUrxxQXf2yW9wjHAQn5c4xQ0iYyktX+aNTe3qaN7eppoORkdKKk3Hb29TRvb1NNooAdvb1NG9vU02igB29vU0b29TTaKAHb29TRvb1NNooAdvb1NG9vU02igB29vU0b29TTaKAFMjAElsAd6h/tC3/5+4v+/gqvrBI0PUCDgi2k/wDQTXi8ems+nW90bpV82byypb7q8Dd+ea2pUlNXbPOx2OlhpKMY3ur7nuH9oW//AD9xf9/BTmvIlClriMBuQS45rw6TTglr532vB2sxRjzxxj8yPz9q0ruBr638N25m8tXtm3OT90eY2T+VaPDruccc3m0/3evr5pfqeu/2hb/8/cX/AH8FOW8ifOy4jbAycODgV4dFpweWaJ7sLJG5Uc8EY65rQ0WM21xq8az+YBpkx3KeOgI/TFDwyS3CGcTlJJw/E9f/ALQt/wDn7i/7+Cj+0Lf/AJ+4v+/grw8aY32KzuGugPPkKMoblBxg/wA6W609LWCRzehmU4Cg53H/AA/wp/Vl3J/tmpa/s/xPdVlLAENkHkEd6s1ieHyT4c0sk5JtYv8A0EVt1ytWdj3YS5oqXcxNO/5GfUf+vO2/9DnrbrE07/kZ9R/687b/ANDnrbpFBRRRQAU2QMU+Q4bg9M/hTqZLs2fPnG4dM9c8dPegDJguddbAuLGJMFcmNwc/MM9T6Z/Idc8LDda4Qhm0+FTkb1WQHAzyQc/5/SqVmkh8gy+I0kIRCygjDZJwQcjryP8AOKkhh2CEt4lWQ5GCWX5zk46N9fy9qALcct47Bb+xUxhAUbG9jJ7gZx9f8iOO71xsBrGHcCu4bsDBznBzjjA6Z+97VDFEwW3X/hI1kYugDcHzMEkrjd3GB+HvWhZsghhjhvIZZERfMbnJTnBxnjr1oAqG810AA2MO9mwvORjBOTg8dAP61YsX1Kdy2oWsMPlYKFCGLHBDY547frUV3KmoLbyWWpLbAurNJn76ENgKDwcn+VRLYXNzGot9XLK2C0seemHxg5IPOB1zhetAGrI07Wfmwx7Zym7ynxy23hSc46479qzo7nXShMlhErjoqyDDfK3U54G7b+ZqOO1nsZ1+0a20nzArHMNgbCt8uc/ievSgiOaaWdNWgSR3DYRzIIx5TDH3gMYy3QUATR3GtH/W2UaggD5HBIOOvJ+v/wBfrSi41F7e4jubONZGQCAY3q7bed2OAM+9R2AeOR/M1RLwsqCAjnDBGySBxyOfwq9dxyz6Y4jlCz+USkoBAVipG7HXv0oAzTc6tDK0VppcIT5sDGwFgqnk9O+O+dp+tatuZUSPzIAry/NIExtjOBnJ7896yJLVyNj+IdjFWBGQCfkx/eyMYZu3X2qeSOS5gBg1OKCK2jZGeN94OVwC2SMEdep69aALd7JqMTj7JAk6sGJ3ELtOBtHXnJzVY3OteUM2ETMd2RvGB8uQPvc88VTto1gmSa58SrNAN4eNmVVf5eec8YyDxW5EwuI5QXSSNjhSgI+Ugd889c5HrQBUhudRW4Y3VrtgUuA0eG3D5drYBJ/vcc1Al5rEokZLFAgdwm/gsAwCnBIxkZP4ds1E9vOjytca0IcySFVY4ABII6kZwox+PqM0kFlKZZI18QebK5Ygcbgfl7BuwHYD73agCRbnXwJd1hDkyYQCQEBcDnqM859Pwq7GLj5ri4tkaZGZVCKMspPykEnjjr+NVLaUWwuRPqttJceYxRnfiNPlBBGex/nU9/NbXOmzzQ6lHAI/la5Rwwj5BI69eMfjQBZvWu44GezRZZQMCNjgHkc59hmqRuNZ34NkmzGcq65GHAxgnuuTmq0gBLRR66A7Bym0Fivzjr83bO3t1FDRERyEa+AzjaJDyFO8D+9jOTj8eMYoA21ZxL5bKxGC3mcAdeB9cVJgZzjmsSJmtbrzW1eOXKsvkSuEXO8jcM5PH3fwpG0zUZGHk6nIsa/Lk5z945OD1IGB6H0oA3KKpWtpc21qqNdm4nDDM0q9V3ZIwD1xkZ//AFVdoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAwn/wCRrvv+vG2/9DnrVtf4/wAKyn/5Gu+/68bb/wBDnrVtf4/woAfONwA2lh3GM1VFtEOlso/4AKsXV5b2UXm3MyxJnG5jgVS/4SPR/wDoIQf99U0m9iZTjF2bKd/E8lwwbT5JlHQ7AR0qsLcgqRpMgK8A+UOOc/zrctdXsL1yltdRysBkhTmrfmL60bboS97WMijHHviiZ7c7wo+8vIpUto4ySlsFJOeEq75i+tHmL60iyq0Qf70O76rmmi3jHS3A7fcFXPMX1o8xfWgCnHAkRJjg2Z67VxUmG/uN+VWPMX1o8xfWgCvhv7jflRhv7jflVjzF9aPMX1oAr4b+435UYb+435VY8xfWjzF9aAK+G/uN+VGG/uN+VWQwboaWgCrhv7jflRhv7jflVqigCrhv7jflRhv7jflVqigCrhv7jflRhv7jflVqigCrhv7jflRhv7jflVqigCrhv7jflRhv7jflVqigCrhv7jflRhv7jflVqigDEbRoHmllKS7pCSfbr/iaYdBt8k4nyQB19MY/l+preoquZmXsafYxRo8AtVt9kuxX8weu7GAab/YkJXGJvugZHB46c1tSSJEheRgqjqWOBTY7iGYkRyo5HJCnNHMx+xp9jGfQrd2clJfmYuR7nGf5Co7vw9BeWscDeeojdnUr1G4EEfka6Gijnl3E6FN3TRTjj8qJI1jfagCjjsKdhv7jflVqipNSrhv7jflRhv7jflVqigCrhv7jflRhv7jflVqigCrhv7jflRhv7jflVqigCrhv7jflRhv7jflVqigCrhv7jflRhv7jflVqigCrhv7jflRhv7jflVqigDOvLZruxuLYq6iaNo846ZGK4D/hV8n/AD/H/v3XqFFXCpKHwnLiMHRxDTqK9jy//hV8n/P8f+/dX7/4fteWenwC6ZTaxGMkp97LE5/WvQaKv29TuYrLMKk0o7+bPL/+FXyf8/x/791f0v4ftp5vM3TP9otng4Tpuxz+leg0UOvUelwjlmFi+ZR/Fnl//Cr5P+f4/wDfuj/hV8n/AD/H/v3XqFFH1ip3F/ZOE/l/FmZp9o1jp1raAO3kRLHux1wAM/pWnRRWLdz0IpRVkYmnf8jPqP8A1523/oc9bdYmnf8AIz6j/wBedt/6HPW3QMKKKKACmuWC5VdxyOM44zzTqzboav8A2gptjbC0CDIfO5m3DP6ZoAy2u9DkkiL2M5K7dhKE7MsVHfjofw/KpJItGls7O+exmYM6xImMt3ADDPI6/hipYpfEjuoMFiiYUsXDA9TnGGPbH596tSwaikSzQiza8YhZGKELt56d8/d6nHFAGXb3ujpLFHDp864cRxFB0yfrxz+Y/IOh1TT4WiS3t76FpCgbaobGR3JJ6YwSPTHbi2reIRHCNtkZcL5pYHZ1OduDnONvUYq5ZrqRJ+2tABhSPJBHPORznjpQBGNN06fbFJArSQiNvLZy2zGdvU/Wqv22x00Q2bW9wGgdcKuW+ZlY5zn5hyfxrXtvNNtEZwBMVG/HrUtAGSl/ZavbTFraV0hQSbXXBbKngc+hIIPrWXHdaEnmkWV0BIyltynDbkOO/QjOc+pz3rppIIpsebEj7eRuUHFPAAGAAAKAM22trFZYrkQeTLOymMmQkuQhxnBIJxu9e9Our6LTZW3wTM07FgU+YHao6noox+HB7nnRooA5u5uNJe6aSfTndmViXBBz8g5Az0IYjPHP51Gl/pcdlGotLuKGZNrwxqHBATcQcZIwD2wa6iigDngukXdhNef2c/8AoyFljf5WxjoOcDO3px0qVtXsrNolkhkDRK0aLCd6qAqscnp04yfQ81stDG8iyNGhdfusRyPpTioYEMAQeCDQBlXhsLyO3mvoB5bozIzNjau3JJx7VYTSLOFzJbx+RIXL70/vEYJwcjpx0q7tBxwOOlLQBQbRtPkjZJLdXDsWfJPzMSCSffKg/hR/Y9n5TwCPFvIcvCMbWPqe/p37VfooAoppFjGhSODYpzkI7L1IJ6H2H5U4aXZjOIiM/wC23qD69flH5VcooAotpFg7Fmg3Fsgksx6nJ7+pP51b8pfOMvO8rt6nGPpT6KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDCf/AJGu+/68bb/0OetW1/j/AArKf/ka77/rxtv/AEOetW1/j/CgDkvib/yLaf8AXYV55Y6ZbXOliZ1ZXBbLlvlPHC/Xg16P8R/K/wCEeTzg+3zRjb1zXlG6z27f9I25zjjFd+H/AIZ8vm1lirvXQ6v4af8AIcuP+uJ/mK9TrzL4cm2/tm48kS7vK/jxjGa9NrnxH8Rnq5QrYVfMKKKKwPTCspfEmjvGsi6hCUbo2eOhP/sp/KtWsV/CmkPDHF9mwkcYjXBxwAwH44duaat1JlzfZNA6lYjObuEYJB+cdutWVYMoZSCCMgjvWGPCOkBZl8hv3ocMS3I3EE49OVFbMEKW9vHBGMJGoRQT2AwKHboEebqSUUUUiiO4k8m3llAzsQtj1wK85T4h6vLE0qaZG0agksCcADrXotzG0tpNGv3nRlH1IrzKLwz4oi01rD7NA0BBABcZGc/4n866KKg0+Y8rMZYiMo+xvbXZX9D0bw9qD6rpFtfSIEeZNxUdBzirP2S4WRitydrMWx6c9KpeF7KbTtCtLS4AEsUeGAOcHOauQpfi9LSOn2bL4Xq3bb/X86xlbmdj0KLk6cefeyuBtLrav+mNuBPP4cU9re5acsLkqhP3R6VboqTUozW94zM0dztGThf5VYt4pY1Ill8wk8GpqKACiqGs2+oXOmSRaXdLa3ZZdkrJuAG4Z4+mavjgUAFFFFABRRRQAUUUUAFFFFAFLVYmmswEi83bLG5T1AcE/oKhtIy2pmZbQ28YhKHKgZOQe30rTooAz5Uv/PcxMNhcfeI4HPT9KaE1PBDOvfBGPwrSooAozR3zXDGNwIjwBnkcf4n9KvUUUAFFFFABRRRQAUUUUAFFFFABUcM3nIzbHTDFcOMZwcZ+lSVH5v8AsP8A980ASUVH5v8AsSf980eb/sSf980ASUVH5v8AsSf980eb/sSf980ASUVH5v8AsSf980eb/sSf980ASUVH5v8AsSf980eb/sSf980ASE4BNUVvJTJFGRGHlUMBz3BP9DVoy5GNkn/fNQi1cKg80HYAFJQZGKasRLm6E0MjSR7mABDMpx7Ej+lSUyKPyo9ucnJJPuTn+tPpFLbUxNO/5GfUf+vO2/8AQ5626xNO/wCRn1H/AK87b/0OetugYUUVFL978KAKWqrrDeWulPaICsnmNPnIbb8mAB/e657VEJNcWGdHht2uHJ8h0P7tB/t5wfyBq/8AifzrJ1bXodIljSWKRw5UZU+u7t3+6aAElTxUoIgfTSSWOZWY4+VduAFH8W7rngiiSPxQ8c4WTT0crKImVm+XIXyyQVOSMNn6ion8T2scSSNDcYYgfKA3BLDdwemVxn3FN/4Sq0WeCF433TSCMMjhlBPfIPT39cjtQBZmTxMFcWz6eTl9hlZjkcbc4X65/wA4GTxOI22Pp7SfvNpZmxy6lMgL2XcPriobvxNa2V81pJFNuDYDZAUjCnIJP+1+nvVdPGNkzMDDPgMoyvzcEZJ47Ac0AamnjxAL3/iYGwNr8/8AqN27tt6j65p12uvG/b7I1itplMeZuLnru6cDtj6U3TtVh1EZRHT5Qw3EfNksOMHn7hq/+J/OgDKjTxLtQzNYbwU3BJGCn7+7qpPdPyNMs4/FQmzeSae0ZkUlYmYELsIIGV/vYP581sfifzo/E/nQBk7PEyQxHzLNnRE8zksXPlsGx8oH39pHTv0p7y69JZW5ihjjukXdcBwNrkoeEw3ZsdcZx1rT/E/nR+J/OgDHsH8UyXKm7jsI7cOm4c7yu0FsYJH3jgZ9KtakmuG73adJa/ZzFgrMSCHyeRgHtj/Cr34n86PxP50AY08fixhKYptNU7v3Q+bAAdSN3BzldwP4U5h4r8uXa2lb8Hyx8+M7hjPH93P44rX/ABP50fifzoAw4W8XyRM7LpcbFDsjfdkNu/iIyMbc9KsbPEoAAksGOCCxLc/PwcY/u+/X861PxP50fifzoAr6mmqM9sdNkgVQ588TEjK47cHn/PNZrx+LGiaNZdNU+UoWTLFt+z5j93H3unH+FbX4n86PxP50AZU7eI5IrYWyWsdxGhFwZj+6kbAwUxlsZB64696dFJ4hSCdJ4rR53QfZ3hzsRtvPmZION3pnitP8T+dH4n86AMZF8WkMHfSgdpwV3n5tvHbpu/SiX/hKkTei6cxAYsgZiSfLXAXIH8YfqehFbP4n86PxP50AV5P7VbR4TD9mXUSqeZ5mdgPG/GPxxWUIvGIWNfP0s7ANzHdlznvxjGPTHNbv4n86PxP50AZdxNr6W0cMFrE90ZjulZgYhGWOO4bIG3tUllJrsTBtRitpU2Y2Wh53bjyd2BgLt985rQ/E/nR+J/OgChp0eui/3ahJa/ZRCRsiJJL7uCcqO2f881Fdx+JGv5GtpbEWuHCIxYNyBtJ4PIP+fTU/E/nR+J/OgDJmTxO24RPp6KwYD5m3L+7+Ug7cZ35PToB60tz/AMJJGJpYBaOArlIdxJY7F2jJA/jDfg3tWr+J/Oj8T+dAEV0NS/s5Batbm9+Xc0gITtuwBn3xWay+Kt67X0vbgZzvznf9P7v61r/ifzqje6kllPDE0buZFZgQcYCjJ6+2aAGxya6bVIZIrYXfmZeYf6ry93QDO7dt9sZ71WSPxZujd5tMztUOgDbc7jkjjP3do6+v1og8S2N1dQW8And5gGXKEDae+fz461Pb6zBdahNZxK5eFirtkAA5I9c9qAIVXxYGiy+lEYXzB8/XncB+lLEvirzYfNbSzHvTzdu/O3Pz498dKhHizTN5jZpVkXIZSOhHUdefwqxZ69a3swiiWUHg5YYABoAs6umtPJbHSZLVFUuZhPn5vlwoGAeM8n6CqJXxcUwH0kMd2SN/90Yxx/ez+lbX4n86PxP50AZjHX2tbdIUgW4jiKzvO3yvJgYZQueMg9dvUcVWjPi+Tzty6ZGVZgmdxDcLtPHb7/XngcVufifzo/E/nQBirH4sUzsZdNZmZ/KBLBVHybeNuez9+4+lW7BdfF4G1B7E2+ZAVg3Zx8uw8jr94H8Kv/ifzo/E/nQBTuo9YOol7aWD7JiICNzg/eJkP3T/AA7QOfWqsS+J1khaZ9PaNdhlSLdluu8DI9xj6c1rfifzo/E/nQBjahP4nNwr6fa2otmYfJOf3igBt2cHHJwBg/XHZYB4reGIzNpkblk3jDHavO7/AIEOPbrWx+J/Oj8T+dAGZbJ4j+0wm6ewMIZC/lMwJG1t45U/xFcdOnbOKsapFq0jodNmhRRFIGWQ43OQNnY8Dkn8Kt/ifzo/E/nQBlXCeKGZvIk05FLSFQdxIGRsHT0yDUt6+uTYOnQwQkIVYXTcFty4I25yMB+uOo4rQ/E/nR+J/OgDPun1y43fY44bdRHj98w3GT1BG4bfqM9eKryjxYWm8o6UFy3lbi/TcMZ4/u7vxxWx+J/Oj8T+dAEzlxt2Kp5GcnGB69KbE0x/1saJ8oPyuW+buOg49+/tUf4n86PxP50AZT/8jXff9eNt/wChz1q2v8f4VlP/AMjXff8AXjbf+hz1q2v8f4UAcn8TQT4bTA/5bCvLYLtYbKa3NurmXOXI5HHGPxr3PWIhMkSMocEn5Su7PSsr+zF/58R/34FdFKuoR5WjyMblssRV9pGVtLbHF/DNf+J7PkceV/UV6wwiRC74VVGSSeAKw9PgS3v0URLE3UjywpIrdkSOaNo5ArIwwynoRWdWfPLmOzBYZ4aiqbdyCe5tLaBZpZFWJiArdQc9KRruxTO6eIYGTlvfFSNbwNAkG1REhUqo6DByP5VnDw5p2wKwLBUCDJ6ep/GszrNby0/u0eWn92l3D1FG4eooATy0/u0eWn92l3D1FG4eooATy0/u0eWn92l3D1FG4eooATy0/u0eWn92l3D1FG4eooAaFCuMDHBpCsmeJMD/AHadkFxg9jVPUra4uVg+zyBCkgY5JwRQBa2Sf89f/HaNkn/PX/x2sYabrCRrGmo4ARVDNyc9/wD61K1jrCxvIL0NIdp2LwD8uGA9OcYoA2Nkn/PX/wAdo2Sf89f/AB2qcUF8VtWeYK622yU9cyHbzjv0P51DBYXyXiTPdARCZ3aJckFSpA5/I/nQBpbJP+ev/jtSDpWI1jrDKUN6uCwOQcEYB4H1OPyqy9leSamkrXJFqkgcRg9cLjH/AH1zQBpUVkW2nXkV7HK84MayyMVJJ+VgMD65H5U0WGrLOrf2huj2jcvTnvz9f0oA2aKwjp2rtCobUPmyucHjHOe3rj8q1LCK4hso47qXzZhnc/rycfpQBZooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACioLzz/szfZ/v8dOuO+PfFTjoKAKL6zpqOyNfQBlOCC44NWoLiG6iEsEiyRnoynIrwO9WFtT1bzSAwkcx8/xb/8ADNep/DskeDISOvmSdPrXRVoqEea55OCzGWIrOm420f5nW0VU01rh7GNroETEkkEYIGTgH8MUkjXX9qwqgP2by28zI79ua5z1i5RRVG0+2f2je+bn7JlfJ3dc4+bH+z0/HNAF6iiigDE07/kZ9R/687b/ANDnrbrE07/kZ9R/687b/wBDnrboAKy9ZXUW+y/2c6IROhm3Y5i5yBkH2rUqOQEtwO1AEdZ2qwXrxLJppiW5BIYyAfMu1sDOPUg1pbW9KNrelAHMtD4mPl7ZolKkbvlQhhz7cc4/D9Z7aHXfNH2l4CgCkhFHPz5I6f3SR+APet/a3pRtb0oAw5xq7ah+7eBLVpEAU7dyKB82cjvn9B61WP8AbrPhLm16R/dK8Hbg7uOhbnird54Yt7y+a7M06OzbiFI2k4A6EeiioY/B9rHIH+03RIZXHzAfMAMHgeoB+tAE+knUhMRfvFKjD92U25Xr6da2Kz4NFjtriGWEkbMbsj72A4z9SXJNaO1vSgBKKXa3pRtb0oASil2t6UbW9KAKl2LszW32Y4TzP3x4+76c/wBKtUu1vSja3pQAlFLtb0o2t6UAU9RW9a0/0B0WcMCN/QgdR+PSszf4mEJISyLhgADnkdz/AJ9a39relG1vSgBKKXa3pRtb0oASil2t6UbW9KAEqG6ExtZfs5Am2nYff8an2t6UbW9KAIbYSi1iFwQZtg8wjoWxz+tS0u1vSja3pQAlYl7Hrvmy/ZJk2HPl5C8cjGePw+me9bm1vSja3pQBkaWusrcn+0ZImi8s8IAMNu/lj+Q9a1qXa3pRtb0oASobvzvsc32f/XbDs5A57dcip9relG1vSgCK3EgtohM26UIN5xjLY5pWhieRZGjVnTO1iORn0qTa3pRtb0oAY0aMpDIpBGMEVgyL4iQNFB9iVclEfHzY5wfTOMduvtXQ7W9Krahp41Cze3d5I93R4zhl+h/MfjQBm6gdXMyPZWtqUDDAn4Yep4PoTS241prwpc/ZPs4G4NGvzMc8ZB6VGnhZRceY9/eOo5CFhycknPHPWtCy0oWEeyKaY5OWL4JPAGOn1P4mgC7RS7W9KNrelACUUu1vSja3pQBVvo7mS3AtJTHKHU545XI3DkHtmqVo+tm8iW6jtRbgN5jJnJPbA7dv1rX2t6UbW9KAEopdrelG1vSgBKhuRM0Si3YK/mISSP4dw3D/AL5zU+1vSja3pQBz1rH4lEkH2ieFk8xfMwqj5dxz29P6e9dBS7W9KNrelACUUu1vSja3pQAlVdRF2bCQWLBbnjYTj156+1W9relG1vSgBB05pqsxYgpgDoc9aftb0o2t6UAZL/8AI133/Xjbf+hz1q2v8f4VlP8A8jXff9eNt/6HPWra/wAf4UAR3v8Ar7b/AHx/MVBeJqravZtayRCyGfPVupqe9/19t/vj+YqaSeKKVI3kw7/dGetAFC5/5Daf9cR/M1DeavbWknll0Lg4YE4xxmprnjW0/wCuI/mapX8GkSzSNclFkH3yep4pq19TOpzcvu7jB4htiGI2EL9456Uf8JFa7gMx5P8Atf59aiWw0KKMoJI1VuSM9e9SRaFo9yWaNUk5+Yg5q/dOb/aO6Hf8JBb7lX5Mt0GetTf2qP8AnkPzpg8N6cHDeV8wIIJPTFWf7Lh9W/Ol7pVsR3If7VH/ADyH50f2qP8AnkPzqb+y4fVvzo/suH1b86PdC2I7kP8Aao/55D86P7VH/PIfnU39lw+rfnR/ZcPq350e6FsR3If7VH/PIfnTJdcjh2+ZGBuOB71Z/suH1b86gu9JtPs7ySqziMFwM+go90TWIS3NDTbpLyJZ0A2suRjuOKvZrK0MxGxj8hdsW35R7cYrTVlARSRkjgZ61L3Omm24JsdmjNLSM6pjcwGfU0iwzRmlBB6UUAJmjcKWql5cG3VSFyWbbknAHHU/lQJtJXZZ3ijeKxW1siYRi1k6nkjg4LDj/vn9ajOvsp+eylTgkg+yg/zOKrkZk8RTXU3t4o3isqXVdk7Ike9A6KXB6bs//W/OtKk1Y0jNS2H7xTXnjjxvbGelJUUofzEdV3bcgjOOtIonjlSUEowODg07NQWwYSSsy7dxBAzntj+lTL1b60ALmjNLRQAmaM0tRvPFG4R5FViMgE80DSb2H5oLAUEgDJIA96hupfIgkl2ltiFto6nAoE9Fcl3ijeKxp9ZMIx9nZzuVdycrz71FLrzxDmylx82D2OPSq5GYuvTW7N7eKN4rLfVkWLeI2Y4c4HXKkDH65qOHVpJ32pbMOY8buMhv8ME0crH7aF7XNjeKRpURSzHAHeshtVkXO6DHyMynPDYJH9P1q+SZ7VHVeW2vg/UHFJpoqNSMtidJ4pG2o4JxnFKX5wBmq6+Y1yjsm0KrDrnOcf4Utu5a5lHoBSLJvMb+4aPMb+4ac7qi7mOB61SutVhtLiON8sJGVF2c/MT3/T86LAW/Mb+4aPMb+4akooAj8xv7hpPNI/hNS01+lAGY+k6ZI7O+nQFmOSTGOTVu3SG1hEVvAsUY5CqMCqTaqVcr5DMNzL8vJGG28+nrVX+33FuJvsUhBjEhx2zng/lV2kzn9pRg7rT5G55x/umjzj/dNZtvqyzQlzEVYSKhU+7Bc/TmoRrjFiptXUhtvzfTOfoMc0uVl+3hpqbHnH+6aPOP901kprJcjMJUlowAepDD+mD+VXNOvDf2azmMxliRtPak4tDjVhJ2TJkvYmIAbr0qwDkVmyiVbJYzHjYFy24diKvW7bowaRoZOnf8jPqP/Xnbf+hz1t1iad/yM+o/9edt/wChz1t0AFIzqmNzBckAZOMn0pagurSK8SNZQf3cqSrjsykEfyoAnrD1qPVHuYjY3MdugKndI+Acbiwx34wfoDW5VLU9Mg1W2WC43BVbcCpwQdpH9TQBjyNqwiiCatZpIWB+dwc8sNvQZ4xz6rQF1oXEHlX1v5ayAyRtNvLLnGAcdc5/PHapj4Ts22AzTEIQV6ccEenof0FTW/huztZA0by8BcBmzghw+fX7wJ/E+tAFe+TWv7RJt7+3SAuCIncBlyowOh6kMfz/AAqqmvxv82q2a7mQqGkznjO0cdznn0qLUZ9Lt9dYXAujceahc5Gx9qqR14xz29/WqMdxossxTbeDd5cTANuDLtwCf7oCnqKAOp0lrzpc3UVxlBnY4Ow5b0AzkbR+BrVrm9EW3tbpBCtwhnAJ37cNkOQePXYT+VdJQAUUUUAFFFFADWkRCoZ1UscKCcZPoKdVa5s0upbeR3YeS+8AYwx96s0AFFFFADXdIxl2VQTjJOOaUEEAggg9CKrX9hDqNqbefdsJDZU4II6Gso+ErExMnnXAJYNuD4PHb6f4CgDfooooAKKKKACkZlRSzEKoGSScAClqK5gW5tpIWJAcYyOooAkBDAEEEHkEUtR28K21tFAmdsaBBnrgDFSUAFRNcwIcPNGp9C4Ht/WpayLvw9aXckrs8iGXO/ZjnPXt/nrQBppPDIxVJUZhyQrAnrj+dSVmabodvpc5lgeQnZ5eGPGM5/z+Pqa06ACkZlRSzMFUDJJOABS1FcwC5tZYC7IJFK7l6jPpQBIrBlDKQQRkEd6ztQS/e8txasyxbX8wggc4+X9f0zV6CJbe3jhT7sahR9AMVJQBhwxeIftELXM9uYFAaVIRhi2egz2qa1GpfbJri6njFn96JVYcKSTluOwx3rWrmNV0LS7O3lu7l7gQ7i0m1uADu4x6ZbH5UATFPE6ShRJavFkhWzzjBwW4+nT/AOvVm0i1iOYNeXELx8fKnBJ+uKxL270bVLpN9zc+eWGDD8ucHAH0yP1q9pFlpcly15bNKgI2KkjALk4PAPtj86AOlooooAKKKKAGSzRQKGmlSNScAuwAJ9OafUN1bR3cPlSZ27lbj1Ugj+VZ9p4ft7O9iuY5pyYgwRC3ygHtj86ANaiiigApGYKMsQBkDk96Wori3S5jCSDgOjj6qwYfqBQAi3VuxAWeIk9AHHP+cVNWHb+FrK2khdJJv3TiQAkckHPPFblABRRRQAU13SNC7sqqOpY4Ap1Vr+yj1CzktZWZUfGSvXg5oAs0UAYGKYqMrEmQsD0BA4oAxX/5Gu+/68bb/wBDnrVtf4/wrKf/AJGu+/68bb/0OetW1/j/AAoAjvf9fbf74/mKle3hkmWV48uvQ46VFe/6+2/3x/MUs635u4zA8AtuN4cHceecfhQBVuedbT/riP5mpHsbaRnZ4FYv97I602b/AJD0X/XIfzNX55RBA8pVmCKWwoyT9BQFrmedNs225tkO0YGR0FTRW8UC7YowgxjAHakutTFtZwXC280nnEAIBhh8pPI/CoLXX7a6mjhEcivIQoyOM4z1/A/lQKyLmPajHtVD/hIFSSQS2kyIrSKHPRthPP44p0HiG1uJoYkjl3S7cZXoCAcn86Bl3HtRj2q1RQBVx7UY9qtUUAVce1NliWaF4nUlHUqw9QauUUAU7K2jtUEUQIRVOAfwp0loszxzBisiL8pH0P8AiasH7w+hpU+4v0oeu44vk+Ep/abiKZYXhLlzhWHTpnn8qW409L1QLliTjGFOAOn+AqWZgJ7cEgEuf/QTU9JpPQcZuLutxkMKQRCNM7QSeTnqc0+opriODb5jY3HAqWmJ33fUKy9a/tD7H/xLfI8/dyJvukYP9cVpkgYyQM8CsXxJHZTWMcV9JMkTzBQYSQSxB9O3X8qa3In8LKrS+IslvsVltCsRuk75O39MVbdtUkt4HSK3EuG80ZBAPbBrDR9HSG/f+07uWHa0Uh3EhQ5VePoSP1qJE0KYxC11a7iEikiNXPy5QnJz0+X+QqrGSlb/AIc3DHrChmC23XIG0DHXA+vStCyN0UkF3s3h/l2f3cDGffrWM0dhp0dzaTXt25mcglmJYdGwD/wIVpQTW0N+0QlZppzjB6cLu/8AZv8AOKk2Ro0UUUhip94/QU5erfWmp94/QU5erfWgB1FFN8xC23euc4xnvQA6oZoLd3Dyom7GAx64pZJisgjSMu2MnBxgVHHH50ryyx4Awqqw7dz+Z/SgabWxBDai5VnlmeRN7BFzwACR+NT3xmFnObYqJ/Lby9/TdjjPtmo7m0nd1a1uPIH8Q25BpupmM6PdG7LRxm3fzSh5UbTnH61XKkk0xSbaMZH8SiJ1+x2DkY2nfgNyc8du1XYn1SW2lDwWqzK48sK2Rjcc59OAPzrFs4tIS+tIbfUr0uzGRIixOCpLEN+ZqqsvhyaCaeHVLuBpMu2GYMf4jgfQ/pTsc6lb/hzoPK1kqDi2DE4IKjpzz/KpYTqeXLCDBBKbcZ+8Mf8Ajuap21vaaddRTm/upt6sVDtuX7xH83/SpYbixtkt7oTyMjKQnHH3ueO3J/zipNkWsakbZQ0MHnYIJ4we4/DpUlqb8Syi5VGTP7sqQOMnr+lXqKRVhP4hUFr/AMfk3+6Kn/iFQWv/AB+Tf7ooAsXMbzWs0UcnlyOhVXxnaSODWBNpKK+JXJud0bh+zMrgg47fdH4A1r215cTzzI1o0aoxVXYjDDcRn9M/jWdG841G3S6WRZWdvMP8PTA2n6nFNTcdupUYOWxqG6xIAChXcFI5zycD+dSm5gW4FuZVExxhM8nOT/7KfyqkNLceeWupJGfBjL8+WwZiCPzH5Uth9muUW7aBUuEADseSpx2Pp8x/OpbSBRbTZo01+lIZYxIqFxuboPWlfpTJOb3+I1upQsFlJGZX2tuwQmTtz7/4VasZNVadory3tYozwDG+T0POPyrAmXQ4pbycalfRN5snmoGOSdzglR6AlsfSpbp9BGosLjULiK5tiilyxABVcfjnr+FXY51K3X8TaWHVS6q3kBfly20dMc/r0pFXV/kV/sy9mOBjk/4YrLt7KwS2W8XVL6SJW8sgsTzyvIrRMtjNmcTzbWnSQg/3iOPwxUs1i79CWJNVMkHmJbhRjzeBknPJH4cVqgBRgAAe1MgmS4gSaPJRxkZqSkXYhu/+PWT6VJa/6paju/8Aj1k+lSWv+pX6UAZenf8AIz6j/wBedt/6HPW3WJp3/Iz6j/1523/oc9bdABUEl5bxTmGSZUcJ5hDcDbnGc9Opqeq0thbzXDTyJudkEZyeCAcjj60AA1CybG27tzk4GJByfTrSy3ttAFMk6KGcRg5z83ofSqQ8N6Urq4tV3KAA2TnAOf5gVYl0ixmR0eBSry+c69mb1Pr2/KgB51KyESSG6hCugdcuBlT0IFKdRsVYK15bhiNwBlAOPXrVaTQdOkWNWtxtjUIqgkAAZwP1NImgaYjhxbKWGMEnOMdP5CgC9G1vcrvjaKUA43KQ3NPEcY6Iv5VBZWFvp8LRW0exGYuRnv8A5FWaAGGKMsjFF3J9046cY/rT6KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigApCAwwwBHoaWigCMQQq5cRIGOASF5p4VR0UflS0UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAYT/wDI133/AF423/oc9atr/H+FZT/8jXff9eNt/wChz1q2v8f4UAQann90QSCCSCO3SqPnXH/PzJ+Yq9qfSP8AH+lZ9AEtsSb1JZHZ2xjJ9K1vOX3/ACrIg/16fWibVTBPLG9rMURgA6jIbp/iKLCbS3NcyocZB49qYPIU5Eag+oWuTk8RzefKFilCryPl7Uttr81zOY1DrhdxJHHb/Gr5Gc31uF7HWFoWGGQEehWkAt1xiJRg5GErA/tC5/v/AKUf2hc/3/0pcjH9agdF5y+/5Uecvv8AlXO/2hc/3/0o/tC5/v8A6UcjD61A6Lzl9/yo85ff8q53+0Ln+/8ApR/aFz/f/SjkYfWoHRecvv8AlR5y+/5Vzv8AaFz/AH/0qtea1c2kathnyTwo9qORi+twOrDh34z0NRTTNGIkXA3jljzjkD+ZFVNGuXu7SOdwQzLnB7dKnuR89uTwvGT2HzKf6Gpeh0RkpJNEj2UboQxYv13k81Gv2qB0i3Kyudqs3UcE/wBKubht3ZGPWqjXCTXUKR5ba5JIHA+VqCiY20b4Mo8xgc5b1qG6eW1RWhIYE42v24J6/hVyqeoyeVArbC3zEYH+6aTstS43k+UnSABt8hLv6noPoKp6tJdR26tZ2qXEokX5WOML/EfrjI/GtGo2+8aZm1dHK/btVtVhj/4R2P5wd5jxgEAEdPetaGMskTS6fDGzq29VQHaRgL+ma06KbZKjbqUrRHnRjd20Yfjnb14GatCKMMGEaBh3xzT6KRYUUUUAKn3j9BUc0csgHlS7Cr56cH2qRPvH6Co55JEXEIUyO+0bug4z/SgadnchxdXsGC32ZgxGVOScZH5UR6VbI4dlLuGLZJ/iPU0n7+zSNfMWRpZMHIxgnJ4p0wms4pJkcSEkZV+B1/8Ar0KTjqik21YtRwpFu2LjccnnNPpkauoO99xPtgCn0EBVe9Li1mMcQlfy22xno5x0/GrFMfqKAOVN7rFurTPoETzAA7oyMliAD+ufwq/Zma5tRLcaTDC5KgIVBO0nDdv7tbNFO5ChZ7lCESSXRWW1QQgtsYpyPukfqW/Krf2eHAHkx4HbaKkopFhRRRQAn8QqC1/4/Jv90VP/ABCoLX/j8m/3RQBdpjoG+bapdQdpI6Gn0UAnYzwdS+ykkRebz9aleSCK0xGuQ44VRyfeoojqTThZRGsRPJXrjH884p8YjsrnygCfNy4PU5759uc/nUqzVmaVNH017Ec8sF1Av2d8TowKDGCD7/rV452DdjPfFIgiaVnVBvHBbHNOfpTTTWhmcxNPqonlJ0KCVBI6ocjJjzkH6nk4qWwubvUGlN5oqQoACC6gliVyeD78Vv0VVyOTXcyv9KBgCWkYVtrSjZ0bIyR+Z/KtH7PDjHkx4/3RUlFIsRVVFCqoVR0AGAKWiigCG7/49ZPpUlr/AKlfpUd3/wAesn0qS1/1K/SgDL07/kZ9R/687b/0OetusTTv+Rn1H/rztv8A0OetugAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAMJ/+Rrvv+vG2/8AQ561bX+P8Kyn/wCRrvv+vG2/9DnrVtf4/wAKAINT6R/j/Ss+tDU+kf4/0rPoAkg/16fWmXOsi2uJYntpSEGQwHDDGTj6U+D/AF6fWmz6q0FzKj2crRpnEgGQ3AP9aaJk7Lci/tCFmBbT5suu/O3PfH86msZoL55ALQxhApy465z/AIUQ6uk5lCwSqUjL5dcA4OKvwyebBHIVK71DbT1GR0pshRTf/AG/ZYP+eS/lR9lg/wCeS/lUtFTcvlj2IvssH/PJfyo+ywf88l/KpaKLhyx7EX2WD/nkv5UfZYP+eS/lUtFFw5Y9iL7LB/zyX8qqakkdvZPLHGgYeorQqObb5LFl3BRnFNMmcE4tIr6HM1xYxysu0sudvpnBrUUAxgEZBFZ2kS+dAJNu0NkgenSrm6ccKiEDoc0PcdL4ENkslfCh2WPOSg6H/Cpo40iXaigCo99x/wA80/Ojfcf880/OkWT0VBvuP+eafnRvuP8Anmn50AT1G33jTN9x/wA80/OmFrjP3E/OgCWiosz/AN1PzozP/dT86AJaKizP/dT86Mz/AN1PzoAlqKe4htY/MnlWNM43McDNGZ/7qfnVLUrVb2BIrpYtokDqCcgke3egTvbQv2lxDdRLPBIskTqCrqcgjmnSwLcIyMWGGyCpwQcVDpsccNqsMQQJGAoCdB/nNTMZVY7FUj3NA15jI7Zzs+0SmXyzleMfifepZoUnj2SLlfSmb7j/AJ5p+dG+4/55p+dA02tUT0VBvuP+eafnRvuP+eafnQInpj9RUe+4/wCeafnTWa4J+4n50ASUVFmf+6n50Zn/ALqfnQBLRUWZ/wC6n50Zn/up+dAEtI7rGjO5CqoySewqPM/91PzqG6V5bWWKUII3Uox3HoeKAH2t9a3u42s8cwRtrFGzg4zj8iKLX/j8m/3RVbSbO3sUaC3WJQfmwgxnAA/kBUzNLbzO8cJkLDoKBK9tTQorMOoXv/QPf86T+0b3/oHv+dAzUqlJi2vJLqbJQoFQgZ2juPqTUH9o3v8A0D3/ADo/tC9/6Bz0AW7aSWV3keIxoQNoPU9ck/pU79Kzf7Rvf+ge/wCdI2oXpH/IPf8AOgDQorN+33n/AD4P+dH2+8/58H/OgDSorN+33n/Pg/50fb7z/nwf86ANKis37fef8+D/AJ0fb7z/AJ8H/OgB82oWc3m2sdzG84UkxhskAEA/zFXbX/Ur9K52yht4pnkS1CPLuG8gZ+Ygnnv0FdJAu2MCgSv1MnTv+Rn1H/rztv8A0OetusTTv+Rn1H/rztv/AEOetugYUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGE/wDyNd9/1423/oc9XQSOhIqk/wDyNd9/1423/oc9XKAA/N97n60m1fQflVTVdUstE0yfUdRnEFpAA0khUttGQOgBPUiprS6hvrOC7tn8yCeNZY3wRuVhkHB56GgCUAA5AFOyfU1WS+tJL6SyS5ia6jQO8KuC6KehI7A0Wl9aX6SPaXMU6RyGN2icMAw6jI7igCzk+poyfU0lFAC5PqaMn1NJVG41iwtdXs9Kmn2314rvBEEY7lQZYkgYGM9yM9qAL+T6mjJ9TVDTNYsNYF2bCfzhaXL2sx2MoWVMbl5AzjPUZHvV6gBcn1NGT6mkooAXJ9TRk+ppKKAAfL93j6Uu5v7x/Okqtf39tpen3F/eyiK2t4zJLIQTtUDJOBzQBa3t/eP50b2/vH86q6dqFrqunW9/ZSiW1uEEkUgBG5T0ODzVaHX9Mn1+40KK6DanbxCaWDY2VQ4wc4x/EO/egDT3t/eP50b2/vH86SigBd7f3j+dG5vU/nSUUALub1P50bm9T+dJWba6/pl7rV7o9vdB9QslVriHYw2BgCOSMHgjoaANPc3qfzo3N6n86oaxrFhoOlzalqc/kWkO3fJsZsZIUcKCTyQOBV4HIzQAu5vU/nRk5zk5rMv9f0zTNTsNNvLoRXmoMy2sZRj5hXGeQMDqOuK0qAFBIJIJ560bm/vH86SigBd7f3j+dG9v7x/OkrNg1/TLnXrrQ4boNqVrGss0GxgUU4wc4wfvDoe9AGnvb+8fzo3t/eP51na1rmm+HdMfUdVuRbWiMFaQqzYJOBwoJ60Xet6dYz6fBc3GyXUJPLtVCMxkbG7sOOOcnAoA0d7f3j+dG5v7x/OkooAXc3qfzo3N6n86SigBdzep/Ojc3qfzpKKAF3N6n86CSepNJRQAuTnOTn1o3H1P50lFAC72/vH86N7f3j+dVV1C1fUpNOWUG7jiWZo8HhCSAc9OoNWaBtNbi72/vH86N7f3j+dJRQIXe394/nRub+8fzpKKAF3N6n86Nzep/OkqKe5gtghnmSMSOsab2xuZjgKPUk0AlfYm3N6n86Nzep/OkooAXc3qfzo3N6n86SigA7Adh2pdzf3j+dJRQBV07/kZ9R/687b/ANDnrbrE07/kZ9R/687b/wBDnrboAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDCf8A5Gu+/wCvG2/9Dnq5VN/+Rrvv+vG2/wDQ56uUAcZ8WP8Akl+u/wDXFf8A0Na4i21LxV4Wg8A3cuui5sdVNtaSaf8AZlVIo2VAuG+8SAevqPTivVPE2gxeJvDl7o00zwx3SBWkQAlcMDxn6Vk6l4EttSsfDNq97Mi6DLDLEwUZlMYAAb0zt7UAcB4d0fXJ/i/4yit/Ej20yIvmTC0RvM3p+74PA2ZX67eetYng+91vw98Ddc1yw1ho9k6rbw+Qh8l/NQM245zuDYwRxXrEfgb7N8QbjxVaavcwC6Ci6sggKTbV2jnqBwD9a4rxJ8PLjwv8MvEljpmoX2o21y0T29h5OTGfOQkjbyxwPTtQA+81vxh4XPhfX9R1+PULPV54YLmwFsqLF5i5Gwjk4Gee5HvWZ4n8ZeI9N8V6tBq2ualoEMc5XTGj05ZbWSMHhnbBZiRgnAOOfTFdToXwzEo0C91XW9RvLTT445rXTbgALDJtBwT1IU9AemMdKu6r8N7m9udTFn4q1Kz0/VCTd2bIsynPUIzcoDnt/hQBr7dY1ux8PX+la/bJArRzXrwwBkvI+Mhc5KZwfcZ9qwmvo2+JfiXW5ubbw7pCQe258zMR77QBW7/whyW1loFjpmq39haaRIreVDKQLlQQSsmMZBwc/U1Un8BibQfEmnDU5Fm127e4lufKGY1Yr+7AzyAqkde9AHFaLf6r4N0jwHezXZTTNWmk/tNGRcGWfLxuWIyMBuef4aybP4sa6mleKLm9uCHuYPtOhqY1BRGmaLgY+bBIPOfuGvWvFPg+x8UeFDoEztbwDy/KkjALR7CMYz7ZH41h3fwo0a7l8Ms08qjQo0jChRi4CkMN/pyCeP7xoA5698X+IfBep3lrr+pNcibw+Lu1LxooW7VQGUYAzlsnnPGKy9N8feKb7QtL0GS/aLxPca19lllMSblt9oYtt244DDt0FejeN/h/p3jk6a19NJCbGQsDGAd6nG5TnsdopIvh9psXxFk8ZCWT7U0ewQbRsVtgTcO+dox+NAHBat458TaXaeIvDy3zS+IU1iG206Tyk3mGX5k+XGD8qkEkfxj2rT8L+ONW8W+IvCdlaXpWFNMa71fai/vHBMeDx8vzrnAxw1dVf/D/AE3UPiDY+L3mkW5tUCmAKNkjAMFYnrkbh/3yKh8OfDjT/DE+vT2F3OsurbgHACm2UliAhHoW6/7IoAzfij4vv9BudE0nT7xrBtSlfzr1LczvDGu3O1ADknd6dvxrmYvE+q6j4X8baNfXdxqdpb6Y81nqM9k1szqVwyMpA5BI/I/h29/8O49R0HSbSfXNRbVNKkaW11Zn3ThmbJ3ZzuHQY/2RTrTwCy6PrdpqOv6hqN1q8RimuZiAEGMfJGPlH/1hQB51DqHizwf8NPD/AIph15JrJFijbSjbqI/JPAG/7xbjk+/tz2+kazd3Pxr1rTWkT7FHpcc0aeSgYMfL6tjcep4JxVaP4QW/kadp114h1K60OyZZRpsm3Y7jqc9QpOTt7ZOCK6RfBsKeMNU8Rx306XGoWX2MooAEQwuGU9c/KKAKfxF1a40vSrMW/iGDRPPuAjzNAZpnXusSAHLfh6ciuK8OfEDWdOXxjFfXVzqUWk2i3No99afZ5iWHAdRg4JIPPOOe9dZqHw3e/wBJ0SFvEeonVdHmklt9TlxLIxdtxDBjzjCgc9BUml/Da0stR1i8v9Tu9TbWLX7Pei5Cgv23AqBjjgAdOPSgDJ8MWnj69tdD8QnxPbXcN8Enu7C4t1jjWFhnEbKCdwH056579X471K70jwNrGoWExhuoLcvFIFB2nI5wQRXN6f8ACaO2udOjvfEWpX+laZMJrPT5doVGHTcw+8B6YH5cV0D+D1uk8Qwahq1/eWmsjb9nlkJW1GG4izkD7wPT+EUAclr3izW7PwV4Dv4L8pdancWiXknlofNDplhgjAyfTFcZ4n8Y3fhH4n+Mzp6hby8S2iS5ddyWw2JmRhg5xkY46nv0PeRfCPdbaVBe+J9Ru4tKuEls42RQkaqc7cd+3JPAGBitx/h/ptxrniLUbqWSdNcgWCeBlACBVAyp654B+tAGL4nguLi38F+GrnUW1Oe/1BLi4uWVVE8UK+Y+AoAC/dx7Y69aox6h4u8b+JPE8WjeIV0a30W4Nrbwrbq/nSLuBMjHkAlffg9OOek8P+Av7E1HSrqbV5r5dLspLS1SWMAqGYHdkHso2gY6YqlrHwxW81jUb/SfEF/o66oMX8FuAyTdQSM/dJyeeep9aAOa8cw6/J4r+HMFxdWkOts06PcwoXiVv3YLqpxnjnB4z7VZsvF2veF9b8ZaVquonWU0iwF7bSyxLGxYqp2nb2y4/LiupX4c6ZBc+F3tbieGLw9v8mM4bzd+Mlj65GePWrDeBbCbxRres3M0ky6vZizntmAChNqqcHrnC/rQB5ho/j7xHHLouptrd3qrXs6Lfab/AGU0ccMbfxRybQDt46HnPcVsTav4v1nxB45tbPxGbC00T97Cq2yOx+ViEyRwvynJ5PT3rotJ+GcumXWno/irVrjStOk8y1sSQgX0DOvLL7cflWrZeB7ay1DxRdreSs3iBQJVKjEWFYfL6/fPX0oA8zbxd40g8G+HvGs2vRvHdXq2r6etqio6AspZm67iYyeMY3cYxWlJp+ran8dvEttpWsf2UxsITLcJCsjldseFUNwOcZPt711U/wAMrOfwJpfhU6jOINPuRcLOEG5zudsEdP4z+VLqvw4N74rvvEth4gvtN1K5jSNXgVSqKFCkEH7wOAceoFAHnHiPxHqus/CTxLp2tTJc3+kaqlo9yiBRMBJgEgcZyD+GK7u5nF58S9MLAvBoGiSXrY/56S/IB9dik1MfhTpv/CDXPhoX91m7uBdXN64DSSyAg5P5f5NdBo/heLS9Y1jUnuDcyamIUZWTAjSNNgUc89yfrQNb6mZo8HiPU9J0/XU1v/SLnZO1k8ai3ETHOwYXeGCn72TzTbI634lj1HUbfWprARXMsFnbxRRlMRnbmTcpLZI6AjFWY/BbJbxaa2sXL6JDIHSxKLnCtuVDJ94oDjjrxjNSzeE50mvV03Wrmws76RpZ7dI0bDN94oxGUz+PPIxWVpf0zvdSld2a8vd2Xbbf5fPUxbTXtX8S3Xh+O2vm0+K/06We58qNWYMrKMoWBxznrng+vNQpN4im0bxDOfEMyNok08cBSCPM/lqHBlyvPBA4x6811lp4Zs7HUNOubVmjjsLRrSKHqCrFTknrn5f1pI/DcMen65aC4crq0ssjtgZjMiBCB64xmlyS6h9YpJ+6lb0Xf/IxBe6vrniGws4dTksLSfR472cQIhfezYwpYHHX36e+arS+ItYtYbjRftayaiurRadFfPEOI5E8wOVHBYKCPQnFSy6DfL41so7C9uLQWmiJCt0IQ6ORJjawPB45xkHgVrf8IbaNpEtpLd3L3ctyL1r7IEonGMOOMDGMAYxii0mU50Y2va2nTz3f+RW87UtA8Safp82qT6jaalHMFNyiCSGSNd+QVUZUjPBHHFUm1/Ux8JrfWftR/tB0hLTbF5LSqp4xjoSOlblj4dlTVY9T1XU5dRuoY2jg3RLGkQb7xCr/ABHGCfSspvALtpX9jnXbv+yY3DwW3lplMNuAZ8ZYA9Bx29KbUuhMZ0G1zNaNX03te/Ttbtcex1jWPF+tadFrM1jZWcduyCCJC+51J6sDxwSf5is+y1/WNU0zw/p320QXt/PcxXF5HGu7ZAWBKqRgM2B2454rr7TSI7TW9S1NZWZ75YlZCOF8sEDH1zWUPBsMWl2ttb300N1Z3MlzbXaqNyM7MWBB4KkMQR3ocZf16ijWpbNLpbTryu/rrYo6JaXdl8RdRhu7573/AIlsJilkVQ+zzH4baACc55wOMV2lcxb6Ynh3ULrXtT1O7vZ7iJIJCtsWxgkjakYJA59/XPNWf+Ex0f11D/wWXP8A8bpxairMyrRlVlzQV1ZLReRiWt/q8t54g1SfVZRYaPdTKlnHEn71VjDFWbGccjHfrWRB4m1gaba6tHdazd30mySWwGlMLZkYjKIwTIIB4bcc4967rTdFtrWLUvmaaLU7h7mRJFxjeqqVx6YXv61lw+DriO3g09tevG0iBlKWuxVfapyqGQfMVHHHXHGalxl0OiNajd8yXTp0tr07+nqQous6z4q1+0XW57KysnhEC28cZbc0SscllOVzzjvnrxVC08Q6vrUejaUt0LS7uJLlLy7ijBOIG2nYGyAWJHOOOasQaNqlx408TXNpqVzpyyPbqGECuko8leRuHUHIyPxFab+DrWLT9Ng0+6ntLrTmZre6GHfL/f3g8MGzk/0otJ7f1qDnSjZO2y6be71011s+pjalq+r6ANe0ttRkung0s39ndSonmR8lSrYAVuQCDiqmu6bqzadoNxe6/dSy3OqWrbEiiVYWbuvyk8ds59810R8HrcWuqfb9Rmur7Ubf7NJdMir5cfOFRRwByT7mrusaAuq6Ta2aXcltLaSxTQTooYq6dCQeD9KHBtCjiKcZRtbzdvJeXc141KRqrOXKgAs2Mt7nFc/4jvr7+1NH0awuTaPqDymS5VAzJHGu4hQcjJyBkg45p48V6ZbAQTyX8k0fyO40y4wzDgniPHX04plxDZeLI4Lqxubu0u7CXfBcm1eNkJGCNsijcpHBFW2mrJnPTpyhLmnHT008vxK16NZ0LR9SafX7f7ODH9lvb1AZIgThwwVQHP8Ad45J5rN0fW7hPF2n2MGp6nf2N7DMXbULTysOgBBjOxCRzyMYHFaU/gkXkFxJe6rcTanLLFKt4EVfKMRygVOmAScg9c1Yg8MXB12x1i/1ie7urRXRV8pUj2suCAo6HvnJ6CptK5uqlHld2m9enlp07+a9Cn4HfWNT06DWtS1eSZZhIi2oiRUAVyoYkDOflP5+1dfWdoWkR6Fo0GmxStKkO7DsME7mLf1rRq4Ky1OWvNTqNx26dNCrp3/Iz6j/ANedt/6HPW3WJp3/ACM+o/8AXnbf+hz1t1RiFFFFABRXI+IviJpPhrxDDoVza6lc6hPbfaYYrO380yjcy7VAOd3yk9MYHWsT/hdvhttOkvItO12X7OxW8iSxy1nzjMp3bVBPuehoA9JorlNb+ImgaJpGmag8lxdjVFVrG3tIjJNcAgH5V49R1x1xVCH4had4i8N+Iv7N+22Gq6dZzPJaXsPlTwkISrbcnvjvxxnqKAO6orzHwz8Q4NI+Fvh7VPEE93f6jqBeOKKCPzJ7lxIwwq8ZwMDt29a6XRPiBoetafqd0Tc6e2ljN9bahD5UtuME5ZeeMA9M9KAOporgdJ+Lvh/VdUsbM2Wr2UeoP5dleXlp5cFy2cAI2TnJwOnetnTvG+l6o3iEQRXQOgyPFd70Ubiu7Oznn7p64oA6WivJ/FPjybU9O8C6v4fubq0s9V1hIZEcBWeMSbGVgCRjIPet/Xfipo+h6rfWA0zWtQbT1DXs1haeZFbAjPzsWGOP5H0NAHc0VS0nVbTXNJtdUsJPMtbqMSRMRgkH1HY15xJr13d3XijUr+68Qafbae3lxrB5YSFdqcFc8yEtn0x37VMpcpvRoOrez2/4Y9UorhNW8RajpmpeMJIWlmWx06Ca2iChhG5WTLkEjjgE+wrTt/F8cOi6TJdWV/LqN9EPLtI4lM0uANz4DbQvOckjqKXOhvDTSTWt/wDK/wCR1FFc43jXTI9Gv9Sniu4P7PZUu7aWMLNEWIAyM4IOQcgkEVS1DxTLqGlQQWFpf2F1qN4tlA91D5bBSNzSqMngICRnvjIp86EsPUb1Vv6/y1OworC1DxLbaXfLpcFnqGo3iQiV4bRA7Rp0DOzMBzg98msDUfGsba14Yu7BryayvY7xXtIYiZJJECAKVPQqd3UgDk5xQ5pBDDVJ7L+rN/od5RXP2njDTJ7DUru5W40/+zTi7hu49skWRlTgEg5HTBOaLDxbbXd/b2dxp2padJdAm2a9hCLNgZIBDHBxzhsGjmRPsKivpsdBRXISfETS44p7j7DqjWdtO1vc3S24MUDBtp3HdkjoflB4Izir9h4vs7/VoNONlqFq91G0lrLcwhEuFXBOznPQg4YA4o549xvD1UruJ0FFeeeGPHoi8L29xqtvqc6RyyJc6j5AMMZ81gNxzkgDbkgECt/T7+5m8favZtOz2kVlbSRR5+VSxfJH1wKSmnaxU8NODkpdP80v1OkoorlbfxNYafYardz3N9ciPVZLRI3jDOZcgCKIDqPTPPXNU2luZQpyn8J1VFcTaeKLi+8ex2httRs4YtLlmlsriIBiwkTawCkhuMgYJ7ir/wDwm9v/ANALxH/4KZv8KlTRq8NUVtDp6Kwry+vNNXVNQkDNBsi+yxPxhiMEY6jkjNObSdS+zGYaxc/btu4D5fK3em3HTtS5+iRy83Sxt0VhXeoy2eq6St7PFbJJFIZwXATcAuOT7k4ovNYgl1PS4bG/hkEk5EqxSq2V2nrj3o9ohc6N2iuVhnS5vtRF3r01oYrpo441nRBtAHZgT3NT6k8lt4eZrDVZZ3edFW4aRXK5YAjKgDH+NL2ujdhc+lzo6KxYdWceF/tzjNykZjZfWUHbj8W/nWQt9er4ftvtF/Ikh1H7PLOGAITcQeTwKHVS+643USOxormorhrfVbOKx1h9RWVyJondJNiY+9lQMYOPrmtDSrmabUdVjkkLJFOFQH+EbQcU1UT0BTuatFUL+C9ubiCKC4a3tsEzPHjeT2AyDgdcmsn+0rizs9cQ3LTpZKBDO4G7cV+6SOCQcfnTlPleo3K250tFc5o88Ut5Ev8AbtzcTBNxgkUKr8c4+UZA68Gs7X7zVfD/AIWttdmuJWk0248y8izkTW7OVYEeqqQ4PquO5ojPmCMrnaUUxpokgadpFWJV3lyeAuM5z6YrkIPiVpU32e4fT9Wg0q5lEUGqzWwW2cscKc7tyqTwGZQOetWUdlRXJX3j+ytNa1HSLbSNZ1G904p9pSytlcKrIHDZLAdDjHUkHAOKpXHir+1Nf8DXWj3UraZqhu2kjA2+aFgYhWB7hh+YoA7qiubXxxpLeCj4q23IsQCDEUHnb9/l7NufvbuMZqO31O41fx9c2UUskVlo1sjXEYOPNuJgSqtjqFQZx6uD2FAHUUV5X4RTxPqnhPwrq8OtahdSXWorLqImkUqsC+aCFGBwSEyOTmtyX4p6PFFdXH9m6w9lZ3L215eJbAxW7q+w7juyR0Pyg4BGcUAdxRXL6b46sNR1u20s2Gp2jXkby2U93bhI7pVGSU5J6HOGAOKqSfEvSESS7Ww1aTR4pTE+rx2wNqpDbSc7txUHjcFI96AOzorjIvEBtvHviAXl/t0iz0q3uhuPyR5MhZ/xCj8qsWHj6xvbyxhn0zVtPh1BtlldXtuEinYjKqCGJUkAkBgue1AHV0UUUAYT/wDI133/AF423/oc9XKpv/yNd9/1423/AKHPVygArOvdXistW0zT3jdn1BpFRhjC7ELnP4Cszx1dT2vhWYwTPB5k0MMkyHBjjeRVYg9uCRn3rHutA0zRPHXhT+zkNv5j3IaESMQ+IG+bBPXnBPfPNRKTTsvI6qNGMo80n/Nb5K51J1qBNSvrV1Kx2UMck0xPAL7sLj1woP8AwIUxtZlji+0S6Zcx2nUykrkD1K5yBXP2wN14dvdYYFkvtQ+0t3JgVgifhtQH8a6+4uIIrOS4lZTAELE9QR/Wo5m29bHNWXLUcVpb8+v4kf2+M38NqoLebEZVcHjAI/xp096kF7a2pVi1wXCkdBtGeayLlZLvXLFrS4a13WjMreWCQuRxg9KJLe6g8QaT9pvmuQTLtDRqu35Paj2kvxX6GPO/xLSaxNNJMLfTZ5VilaIuHQAlTg9TT7vWDY6cLu5s5UJkCeVuUtz34OKoaVbXspv2t7/yE+2yjZ5IbnPXJqxqsciWmnx3Evnv9ui3PsC5+b0FSpT5b/5BeXLc0pLyKOwa9JzCI/Mz6jGaoDXUbT7W6jtZXNzIY0iBAbIJ9eO1UoSWji0MnJjuSr57wr84/PKiqiJI+jaOsUnlOb5wr7Qdp3Pzg0nVl07fjoJzfQ3odVLXcdtc2c1tJLnyy5UhsckZBPNWLS9S8a4Cqy+RMYTnuQBz+tZFxFdWWqadNd3YvFaXykQxhChYfeGOv41a0T/Wap/1/P8AyWrjOXNZlRk72Zau9QFvPHbxQSXFxIpYRoQMKO5J4AqJdXh+xXVxJHJG1rkSxNjcDjP0OexqS/vY7MoEi867l+WKNfvN+PYeprPm05U0q4F9diOa8lVpZVXIByMKPbjHNOUpJuwNu+hetdQmuJgj6dcwqRnfJtwPyNWIroS3dxAqn9zty2epIzj8sfnWYyy6bq1nGl3PNHc71kSZ92MLncPSrWjjfZNcnrcyNN+BPy/+OgURk72Y4t3saFFFFalhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAVdO/5GfUf+vO2/9DnrbrE07/kZ9R/687b/ANDnrboAKKKKAPONT0m/l+P+jaqtjcNYRaO8T3QjJjR90vylugOCOPeuc0zw9q8Xhb4pwvpV2s1/e3LWiGBg1wp3bSgx8wOeMete1UUAeHnQtf0Jfh74pi0O81BdJ077Le6fEn7+LchG4IeSfmPH+yPXIt2mla34l8S+LfF8mh3ul21xokun2lpdR7bi4YoOSnUfdxj3GM4r2WigD55n8Ha/F4K8A6l/Z2sbtIM6XtrY5jvIleQkNGDznA/UduRrad4LvfEOi+MZrbS9es7nULZbe3n127zPdbSGAZCoKfcC5LEYPsa9wooA8n8L+JfFD2/hnw7D4HvbaSxSO3v7zUYCkEaIoUtE38RIGf0561k28ev+G9e+IGmL4X1W+bXZpJbG5togYMOH++5OFxvHHJ4Ix0r26igDwWDw3ri+AfhnanSL4XFjrPm3UXkNugTz2O5xj5RjnJqbxVD4s1bXPFWnanp/ii5jkV49Gi00+XZMhVsGZhgN/CSCTnkY6V7pRQBwnw9uLrRPAfhbTLzSNTW4lV4ZB9nIFsQWOZc4Kqexwc5FUdY0nUZfDXjyGOxuHlu7zdbosZJlGyMZUdxwenpXpNFTKPMbUazpO6Xb8Hc4TUtKv7nUvG4itZSLzSIobdiuFlfy5RtB6E5I/Osqezubibw/rUmn67Faw2BsLmK1WSK5hcFSG2L8zISCOM9jXqFFS6aZrHFuPT+rWPLL7Rp7zwt4nns9I1YPdi3iia+lkkuLlUcEkxtyoGTjPJHYV1HixTb6/wCFdRb/AFEF+0MnoDLGyKT/AMCwPxrq6qalp1tq2nT2N2paGZcNtOCD1BB7EEAg+oo5LLQPrXNJcy01/FJfkjmZZbjw54z1bUZtOvruy1OGAxy2cDTGN4wylGVeRnIIPTrUMSatqPijwtqV7pbWoRL4uiqT5KsE8sOegYgdPXNdqilEVSxYgAbm6n3NOp8hH1jrbW1r/K35HnHiDw7qWrXHjWK2tpd1wLGS2JyizmIbmVW9eMexIp1jaR6lrek+VpniaT7PN9olk1a4mSO2Kg4ID5Dtk4+XjBPNei0UvZq9y/rcuXlt/Vkv0POJNJ1A/C7xHZCxuPtU91dNHD5Z3uGlJUgdTkc1v6rZ3Mni/wAJzx28jQ24uvOdUJWPMQA3Htk8V1FFPkRLxLbvbv8AirHlthNqa/DmbwufD2pDUbhJ7eItbkQ4kdvnZ+i4DZweeOnNdLoOl3On+MtQLxym3XTbSBJypCyMm8HB7np+ddbVW/shf23km5ubf5g2+3k2N9M+lJQtbyKliee6tbmvf5tf5FqvNDo+pxRXOoJp9xK1l4nlv/s4XDzQldpZAfvH5sj1wa67/hGF/wCg1rX/AIGn/CtwDAA649abjzbkQqql8OtzibWe51v4gRXsemajZ2KaVNALq4tzExcyIcAMMjA6ZHPPpWh/wh9x/wBDd4j/AO/8P/xqunooUF1B4iWnKrL7zP1bT2v9Fms0kPmFRsdv7ykEE/iKpnXLs2xjGkXovsbQnlfu93rv6ba3KKHG7unY5Wru6MGWzuZdW0ZruMTmOGUTuE+QMQv4delSalYganpMlvagBLgmRo0+6Np64raopezQuRHKQeVaX+pfbNFubkyXTPHItp5gK4Hc/Q1ZvsXuihLPTri3CXMR8poNhwGBJAHb3roqKSpaWuLk0sc6bC5HiFrYRN9gaYXxfHG8DG3Pruw1UprS4TR4t9nPJs1YzPEsZZjHvJzjuMV19FDpIPZo5eaMahd2X2DR57SWKdXe4kgEW1B1Hqc9MVJb3cmm6tqnmaffSrNMHR4YCykbQOtdJRR7PrfUfJ1uc9quqXksFtHaWeoRRz5M0iW5Mkag4wB2Jx17Dmn28gGjT2+naTMphAIhvItolyeevU8H8cVvUU+R3u2HK73uYE0kmsajpxhs7mBbaXzpJZ4im0YI2jPXOe3HFUviZN5Xw51qMLvluYPssKDq8kpCKB75YV1lUNQ0i11O6sJ7re4spvtEUefkMmCFZh3K5JHoeewqoxsUlYx76Ga90e98ILb3aSyaM0Y1Ax/6PuZTHtD5zvB+bGOlcZqEuq698ObfwRH4c1S21V4YLKeSa2K20AjK7pBL91lwpICknpxXrVFUM5Hw3Y3Vt488aXU1tLHBcz2hgldCFlC26qdp74OR9a5PwromqWsfw5E+nXUX2KbUjc74mHkh1k27+PlzkYz1zXrVFAHk40a6/wCFov4YCZ0X7YPErYPQ4K+WfYzDfj2NdJ4ZU2fxF8ZWc3D3D2t7CT/HGYvLJH0ZCPyra0PwxZaDcXt1DNd3N3eMDNcXk5lkKrnagJ6KMnA96tz6Raz6zaasd6XdtG8QZDjfG+Mo3qMgMPQj65AOa+Ev/JLtD/65yf8Aox656TRdT/4VF4vsBp1z9sub2+eGDyjvkDTEqVGMnI5Fd14Z8LWPhSxay0+e8e2JykdxcGRY+pwgP3QST0rboA4rXdMu7rxf4Kkit5jBbrdrPKiHEO632ruPbJ4Ge9czay6tp/wym8Ct4b1OXWBay6cjpbE2sgfcol877oXDbiCQc8Yr1uigDyXUvBeq3n/CS6RBHITL4dsrOC5YFY5pYy+VDHjnAz6bqbaWC6re6JbDRPGElxFdRT3K6pdzpbWhjOd4ZsrIQR8oXOfUV65RQAUUUUAYT/8AI133/Xjbf+hz1cqm/wDyNd9/1423/oc9XKAIrm2gvbWW2uYklglUq8bjIYHsaxrXwboVncR3ENpJ58QZYpXuJHaNSpUhSzHAwx4reopNJ7lxqTirRbSILayt7OwhsYIgttFGIkjPICgYA568VUXQNMRwwt8hTuCF2KA/7pOK0qKTjF7ozkuZ3ZEbaFrpbkp++VSgbJ4B7USW0Ms8M7pmSHPltk/LkYP6VLRTsgsjOfQtOeR5DA252LsRK4yT1PBqVNLs44UiWI7ElEyguxw46Hk1copckV0Fyx7EC2lut412IgJ2TYz9yPSoX0ixktY7ZoP3Mbl0UOwwTnJznPc1doo5V2HZFG30iwtZxNDbgSgYDMxYj6ZJxTJND06WaSVoDvkYsxEjDJ9eDWjRRyRtawuWPYz5ND06Uxl4CTGmxT5jZAznHX3qSPSrKK3lgWDMUv31di2fzNXKKOSPYOWPYow6RZQbykbbnQxlmkZmC+gJPH4VcjjWKJI0GEQBVHoBTqKailshpJbBRRRTGFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBV07/AJGfUf8Arztv/Q5626xNO/5GfUf+vO2/9DnrboAKKKKACiuO1XUta1Txq/hzR7+PTYrWyW7urswCaRmdiqIqtwBhSSTnsKrjWPE+m634W0rV5bR5L28uoZ5YEwLiJImeN8H7jHAyATQB2d3d29jaTXd3MkNvCheSWQ4VFHJJPpUkciSxJJGwZHAZWB4IPQ15t421m8uIfHmjyOps7XQVmiUKMhnEgbnv90VV1HxtLbarY6BFrK6Nb22mQTzXQsmuZJHcfKirghQAMkkc5wKAPVaK5PwD4lufEel3n2tllmsrprf7VHC0SXKYBWQI3K5BwR6g10eoXkenabdX0oJjtoXmYDqQoJP8qALNV7S/tL8TG0uI5hBK0EvltnZIv3lPoR6VxWiS+ONS0vSPEI1KxkjvvKnl0o24RI7d8H5Zcli4Ug88E8YrnNHXxPDp3jbUtJ1i2soLHWdQmjga0EpnZTuIdiflU4AG3nqfagD2CivLdV+Is091oVimox6It7pEWqXN0LRrlh5nCxIuCBzuJJB4AqB/iFrX/CH6xLaTwXd/p1/awQXrWrRR3ccroASjcqeWU49MigD1mq93f2lgITd3EcAmlWCMyNjfIxwqj1JPQVwMtx40tvGVv4bPiG0lW+snu/th09Va22MAyom7DZ3LjdnHPWsLWdV13VvDNtbTz20+raZ4wgsYrkxbElZGDI7IDx94ZA9KAPYndY0Z3YKqjJJ6AVDZ3ltqFnFd2c8c9tMoeOWNsq6noQa4mXUdd0PxBFoesalFqltqthcyW84tlheKWJQWUhTgqQ2R3471p/DP/kmfhz/rxj/lQB0V9f2mmWUl5fXEdvbRAF5ZW2quTgZP1IqxXLfEbUZtJ8AarfW6QvLEiFVmjEiHMijlTwetUZdQ8Q+IPF2s6bpGqQaXaaOIo2drUTtcTOm/nJGEAIHHJOeaAOli1/SJmtVi1K2c3UrwwBZAfMdM71X1Iwc/StKvMvhzoCan4c0XUdQ+W90rU7+UJH9wu8kqMOecAnI+gr02gCtFqNnPf3FhFcxPd2yq00KsC0YblSR2zg4qzXDeMYZPCel+JvF+kv8A8TS5htkxIoKjy32jA9w5/IUS6h4l8Na1oX9sapbajZ6tc/Y5Y47URfZpmRmQxkHLLlSDuyehzQB3NFeV2XizxLB4Z1jxhqOoW8mn6dJdwxaclqAZykjIjNJnK84GAOgz1NQ2Hj6/t9R0dpPEEesG9uY4Lyyj0uSAW+/jfG5HIUkZ3ZyMnigD0b/hI9G/t/8AsIalbnVdu/7IHy4GM8jtxzz2rUrzr4e2OoQ+LPGM1zqYuFXUfLkX7OqGR/KjIfI5GAcY6d69FoAKK8pGu+NL3wx4h8QwazZ28Wj3N4IrU2QcXCQMxw7ZyOBtG3HTJPNa2v8Aim5UaRK3iLT/AA/ZXlktxuZBPcySNg7EjIxtAP3sEk8cUAegUV5LD491+fwTPd2tzbXF/b+II9LjuZLVoluI2ZMM0Z5QkPg4x04rU+0eNI/Gp8LHxFayLPp/9ofbzp6iSEK+woqA7TklTls4GetAHo1RXNzDZ2s11cypFBCjSSSOcKigZJJ9ABXlk/xE1aw8LxwXd1bLqp1ufSHvzbs0arETum8peSdoGFHGT6Va0bxC/iLVb7wndaudZ0/UNOlxfCxa2eFj8rRsCApyrZBHoQc0AekQ3UFzZx3cMySW8kYlSVTlWQjIYH0xzTbG/tNTsor2xuI7i1lGY5Ym3Kw6cGo9O06HTtHtNMjy0Ftbpbru6lVUKM/gK4SS21bw1qnhfwR4d1CO2tHsbgyXM8AlddhQ7gOBuO4jnj5s4OKAPR6K8qi1bxvc6V4mca9ZxP4dlmjEosFLXpRPM+cE4QbSB8vck9q1IfEeueKdU03TNJu4dKV9Ig1O8ufIEz5l+7GitwBwSSc9qAO5vL+00+FZby4jgjaRY1aRsAsxwo+pJxVivIvEGtald6ReaJrDwzaho+vadG1zCmxZ45JEdG25O1sHBA44qxqfj2e68S6zZR+I49Dt9Nm+zRJ/ZrXTXEgUFmc4IVQTgAYPBOaAPVaK81s/GuteJbPwxY6f5Om6jqqXEl3O8JcQJAdrGNGxnexGN3QHnNW9U1PxZ4bsYUv7y1uhJrFnbQXiwhHmglcBw6dFYcgEdeuBQB3xIUEk4A5JqGyvbXUrKK8sriO4tpl3RyxtuVh6g1zurazeQeO9O0ZHUWdzptzPIu0ZLoUC89vvGvPdG8YS6L4H8EaLb38emm8snmnvntmuDFGhwAiAHLMTjJ4AHegD2uiuH8C+K7jWtS1XS7i8GopZiOS31BbVrfzkfOVZCAAykdRwQRwK7igApgmiaZoRIhlRQzIGG4A5wSPQ4P5GvKIvG2svrUlve+IbLSNUW+aNNG1KyMUMkIk2jbcYyzFeQQcZOMYqxH/amn/EHx9qEWpgi202CYRG3XDfu5jGM/7JH496APUqK8vh13xdaeFtD1+/1e2m/ti609FtY7NVEEcrDcN2SWJUjsMHOK6XWfEVzpnjewsS4/s99MurudQoLExlMYP0JoA6umJNFI8iJIjNGdrqrAlTjOD6cEH8a4LRLjxxq+j6Z4ki1GxZL0xznSWtwsa27kcCX728Kc5PGeMVzMV/rfhnRfiRrFvqiyzWuoNtVrZQDJth+f8A75OMdO9AHs1Fc14i1m807xH4XsrZ1WHULySK4BUElRCzDB7cgVydj4q8Sx6NrvivUNRt20zSp7yGPTo7UBrjy2ZULSZyvO0cDoM96APUaK8isfiBqFvc6RPLr8WrNeXEcN5p8elyQiASHBaOQjJ2EjO7ORnpUmp+NNZg8QanaXXiGz0G7hu2jsLHUbErbXUII2u1x/tDPQjHoaAPQ7/xRoWlvcpf6vZ2zW2wTiWUL5e/JTPpnBx9KtQatp9zJaxwXcUjXcH2m32tnzYuPmU9x8y/mKwNW0K2tLTxTriMTc6lpgSVQcqPLjcDaffd+grmLyQ6b8IfBniCLifSYtOmBHUo6pFIv0KyH8h6UAepUUUUAYT/API133/Xjbf+hz1cqm//ACNd9/1423/oc9XKACiiigAooooAVfvD61a2L/dH5VVX7w+tXU60AM2L/dH5UbF/uj8qmooAh2L/AHR+VGxf7o/KpqKAIdi/3R+VGxf7o/KpqKAIdi/3R+VGxf7o/KpqKAIdi/3R+VGxf7o/KpqKAIdi/wB0flRsX+6PypJ/9XJ8235Tz6Vi6WxgizcavDdB+VJcf40Cbs7G3sX+6Pyo2L/dH5VCo3qGRgynoQcinbGoGSbF/uj8qNi/3R+VR7GrOvVkEjgagsBK8Anke/6GgDV2L/dH5UbF/uj8qwkinMny6sr7htCn1x2/StSCCWOILJJ5jZPzfjQBZ2L/AHR+VGxf7o/Ko9jUbGoAk2L/AHR+VGxf7o/Ko9jUbGoAk2L/AHR+VGxf7o/Ko9jUbGoAk2L/AHR+VGxf7o/Ko9jUbGoAk2L/AHR+VGxf7o/Ko9jUbGoAk2L/AHR+VGxf7o/Ko9jUbGoAk2L/AHR+VGxf7o/Ko9jUbGoAzfEck1vosslqJBKCvMSZbGecCqng+5ur22u2vEn+WQBPtC4bG0Z7DjOa3lUhuamT7x+lVzLltYwdFuqqnNp2Ksc6GyWeSHOeoRc0i3luwyIJem7mM81ZlLxQ/uIgxB+7nHFV/tV3jIsT3/5aCpNxPtdvtJ8iXAx/yzpBfWxIAglOe/lHinG5u/8Any/8fFTwSSyA+bB5Xp82c0AQR3dvKQBBIMnHMZFWvKT+4Pyp9FADPKT+4PyqjWjWdQAUUUUAFFFFABRRRQAUUUUAFFFFAFXTv+Rn1H/rztv/AEOetusTTv8AkZ9R/wCvO2/9DnrboAKKKKAOY1nw1fy+IU1/QtThsdRNt9lnW4tzNDPGGLLkBlIYEnBB74rNm8BXxsrCeDxBIdds76S/F7PDvjd5FKunl7htQqcAA8YruaKAODPw+vrtfEc2pa+Li812wWzkdLTYkGA4BRd/K4YcE5yCc88XLrwhqFtqlvq2gavFZ362Udlcrc2xlhuUT7rFQylWGTyD0OK7CigDlrO6tPBll5fiPxK1zeXkzzma5JVSeMrGmSEQcYXPf3qdfFvhbWG/s1NVtbhroGHyQT8+7jH45roqKAOFs/A+tQWdjosviYvoFlIjRxR22y5eNCCkTyh8bRgAkKCQMVq2HhL7FoviHTvt2/8Ati6urjzPKx5PndsZ+bb68Z9q6WigDiv+EGvbAaLd6JrEdtqmm6bHpkks9t5kV1CoGAyBgVIYbgQ3GSOadceCL/UvD91Zap4hmury6vYbt5jEfKiEbqwjii3fIvy465ycnNdnRQBiz+H/ADvGVn4h+1Y+zWUtp5Hl/e3srbt2eMbemO9c/ffDqS6sL23h1t7aW414a0k6W4LRMAuEALYOCud36V3VFAHI2nhPUrjWG1bxBq8N7dxWslraLb2vkxQK+N74LMSxwO4GBRpWp6D4G0XT/Dd/rlv9osbdIizqULDHB284z9TXXUUAcdrVzofxC0G+8N6drkAnuYwS6IZCgV1JO3Iz0A696mv/AAtqkXiG81jw9rEOnyagiJexXFp56MyDasi/Mu1gvHcHAyOK6uigDB8H+Gz4U8PJpTXz3pWaWUzum1m3uz8jJ5+br39BU3h3+1vs17/a8zSv9tm+zs0SxnyN2EGB+OCeSMHvWxRQBxnxYjeX4X64kYYsY0wFHP8ArF6VJbeE9WudZ0688Qa7HqEGlsZLSGK08nfIVKiSU7juYAnGAoyc+1dfRQBy1l4JtYvB2oeG724a6tr2S4d3VPLKiV2fjk8ru4Pt07VBZeGfE32jTo9T8VmawsJFcJa2xgmuto+UTOHIK+oAAbvXYUUAcvBBp3gy51jU9S1SNIdWvxOvmJtEbeWq7M5OeEJzxUq+PfCrsFXXLUknAGT/AIV0dFAHK23gz7P4Q13QPt+7+1XvG8/yceV5+7+Hd823d6jOO1Uv+EJ1PT9ZtNV0XWLWK5TTYtNm+2WRmVlj6OmHUqfbJBrt6KAOCtvhvLDplzaTa69w9xrcWsvNJbAMXUoWTAYDBKcEYxnocV0Z8P58bp4j+1fd05rD7P5fXMgffuz7Yxj8a2qKAOIk+HmbC4WHVnhv/wC2ptYs7tYB+4kkJOwqT864JB5GfatXRtF1yHWJNU1zXvtj+T5MVpaRNBboM5LlS7bnPqTwM4roqKAMef8Atb/hLrPyZm/sn7JKbiMxLjzdy7Dv65wW4HHHPUU278P/AGrxfpmvfatv2G2mg8jy87/MKnO7PGNvTBzmtqigDmYPCPkWXii3+3bv7dmll3eVjyN8Sx4+982Nue1UP+EIvtOfSr3Q9YittSstNj02Zri18yG6iQDBZAwKkHJBDd8c12tFAHDn4eNNplwl1q7TanealBqF3eGAAOYmUrGqBvlUKoUckjrzVi48KazY6xqV94b1yCwi1NxLc291Z+eqS7Qpkjw64JAGQcgkV2FFAHH3Pgm6W00Sax124/trSBII7+9U3HniT/WLIpIJBwMYPy4GKrXXgK+1TSr/APtLX5JNXurmC6juYodsNs8JBjCRFjx1zk5Oa7migDjrLwhqz+KLfxBrOuxXVzDaS2gggs/KiVXKnIy5Ocg5znOR0xzVt/h7cabpHh5dL1kQatokLwR3b226OeN/vI8e7ODgEYbIIru6KAOVs9/hUXWqeKvEonlvZERRtMVtDgHCxx5bBPJJJycVOvj3wqzBV1y1JJwBk/4V0dFAHAal4C1rVdOuNCvPEqXOhTzGQi5tDLdohfdsExfHHQMVJA+laF74NupvEOrX9rqqQWmrWAs7q2e23tlUdUdX3DGN+SMc4688dfRQByuoeDPtvgXTvDqai0Nxp0dt9nvViB2ywbdrlCehK9M9+tV7Pwdqc3iaDXte1qG9mjs5bM2sNp5UWxyvTLk54Oc5zkdMc9lRQBwlr4F1m3sLXQv+EmP/AAj1rIjRxJbFLpo1bcsRmD42jAGQoJAxUl18P5LtvFFpLqo/snXsyNbi3/eQzlUG8Pu5Hyfdx+Pr29FAHEQ+Dddu9a0TU9c8SRXbaRKzRRQWPlLKGjKEud5O45ByOBgjHORoWHgu1g8L6roF7Obq21Ge5lkZU2FRMxbA5PIz19s4rp6KAONsvC3iZZNOttQ8WGXTbCRXVbW2ME9yF+6ssgcgr6gAbu9Qat4L13UYNU0xfEkb6NqTuzx3loZ54Ff7yROXAA67cqdvbpXc0UAc14nnsPDvgK7t5ZTFbpZNaQlgWJPllVHA6nFcp9nfWPAXgTwqikveW1lPdrj/AFdtAkbuT6ZYIo929q9QooAKKKKAMJ/+Rrvv+vG2/wDQ56uVTf8A5Gu+/wCvG2/9Dnq5QAUUUUAFFFFACr94fWrqdapL94fWrqdaAGXbTJZzNbrumCEoPU44qraXkgDLc7zhjtZlAYrgckD3zWhTJIklGGH0PcUmnuaRnFR5WhysrqGVgwPcGmSTLH8ucueijk1mSWtxb3cCQz7UmcqzAc4Ck9Oh6detacMEcC4QHJ6sTkn6mmQrdTE+0a5tfbFn/RyfnUDEnoMdR6Vv0UVMVbqaVKina0UvQKKKKoyCiiigCvckCGUsu9Qpyvrx0rlIJNGmRx/YEkb7WYo1vjoCcZ9Tj9RXWTbvLk2kBsHBPQGsKIa7tXN1YSnB5CEbvm/oOKaM57iQa/BbQrF/Zt5DtB/drDkLgj0+v861re7+0wJMqMgYZ2uMEfUVRT+1zcruktPKyNy4OQuev1x/KmL/AGuQ4F1ZPx8rbe+eh/CgE2tzW8w1yGu6/wDZNYlhWxs5Xj2HfM+GPGf61u251U3SmWSzMHRlQHPXqKL60E12HH2XGRnzEBJFTJNrRkVo1JxtTfKzj08WlWUrp2mAqcg+d0I//VXW2Gu/adPhuJIW3SAn90Nw4JHX8KZb6X+9TzksWUElwsQyRzjFbEaQxIEjVVUdABgCpgpJ+87mdGlXi7znf5FU6kBGHMM2DnjZzxUkF59oDERuuP764zVjKe1GU9q00OlKV9xvmGjzDTsp7UZT2pFDfMNHmGnZT2oyntQA3zDR5hp2U9qMp7UAN8w0eYadlPajKe1ADfMNHmGnZT2oyntQ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      ],
      "metadata": {
        "id": "b_kVWYXyBNXK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "En las siguientes líneas de código se muestra como se realiza la búsqueda de la información asociada a la palabra \"aspirin\" en ChEMBL a través del uso de la API \"new_client\"."
      ],
      "metadata": {
        "id": "lvBhz77KqiGc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "molecula = new_client.molecule #Se establece el módulo a emplear (asociado al tipo de información que se recopila)\n",
        "mol = molecula.filter(pref_name__iexact='aspirin') #Se filtra la información y recopila aquella que se relaciona con la palabra \"aspirin\"\n",
        "mol_df= pd.DataFrame.from_records(mol) #Se almacena la información en una tabla\n",
        "print(f\"Cantidad de entradas asociadas a la etiqueta de 'aspirina': {mol_df.shape}.\")\n",
        "print(f\"Las columnas presentes en la tabla generada son: {mol_df.columns}\")\n",
        "mol_df #Muestra la tabla"
      ],
      "metadata": {
        "id": "Ot4IV6U5NwZc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "outputId": "864f9053-db96-47c3-f0c7-9ebec63afc89"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cantidad de entradas asociadas a la etiqueta de 'aspirina': (2, 35).\n",
            "Las columnas presentes en la tabla generada son: Index(['atc_classifications', 'availability_type', 'biotherapeutic',\n",
            "       'black_box_warning', 'chebi_par_id', 'chemical_probe', 'chirality',\n",
            "       'cross_references', 'dosed_ingredient', 'first_approval',\n",
            "       'first_in_class', 'helm_notation', 'indication_class', 'inorganic_flag',\n",
            "       'max_phase', 'molecule_chembl_id', 'molecule_hierarchy',\n",
            "       'molecule_properties', 'molecule_structures', 'molecule_synonyms',\n",
            "       'molecule_type', 'natural_product', 'oral', 'parenteral',\n",
            "       'polymer_flag', 'pref_name', 'prodrug', 'structure_type',\n",
            "       'therapeutic_flag', 'topical', 'usan_stem', 'usan_stem_definition',\n",
            "       'usan_substem', 'usan_year', 'withdrawn_flag'],\n",
            "      dtype='object')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             atc_classifications  availability_type  \\\n",
              "0  [B01AC06, N02BA01, N02BA51, A01AD05, N02BA71]                  2   \n",
              "1  [B01AC06, N02BA01, N02BA51, A01AD05, N02BA71]                  2   \n",
              "\n",
              "  biotherapeutic  black_box_warning  chebi_par_id  chemical_probe  chirality  \\\n",
              "0           None                  0         15365               0          2   \n",
              "1           None                  0         15365               0          2   \n",
              "\n",
              "                                    cross_references  dosed_ingredient  \\\n",
              "0  [{'xref_id': 'aspirin', 'xref_name': 'aspirin'...              True   \n",
              "1  [{'xref_id': 'aspirin', 'xref_name': 'aspirin'...              True   \n",
              "\n",
              "   first_approval  ...  pref_name prodrug structure_type  therapeutic_flag  \\\n",
              "0            1950  ...    ASPIRIN       0            MOL              True   \n",
              "1            1950  ...    ASPIRIN       0            MOL              True   \n",
              "\n",
              "  topical usan_stem usan_stem_definition usan_substem usan_year withdrawn_flag  \n",
              "0   False      None                 None         None      None          False  \n",
              "1   False      None                 None         None      None          False  \n",
              "\n",
              "[2 rows x 35 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-2c3ed0e8-0426-4184-8aa4-9127ef59456c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>atc_classifications</th>\n",
              "      <th>availability_type</th>\n",
              "      <th>biotherapeutic</th>\n",
              "      <th>black_box_warning</th>\n",
              "      <th>chebi_par_id</th>\n",
              "      <th>chemical_probe</th>\n",
              "      <th>chirality</th>\n",
              "      <th>cross_references</th>\n",
              "      <th>dosed_ingredient</th>\n",
              "      <th>first_approval</th>\n",
              "      <th>...</th>\n",
              "      <th>pref_name</th>\n",
              "      <th>prodrug</th>\n",
              "      <th>structure_type</th>\n",
              "      <th>therapeutic_flag</th>\n",
              "      <th>topical</th>\n",
              "      <th>usan_stem</th>\n",
              "      <th>usan_stem_definition</th>\n",
              "      <th>usan_substem</th>\n",
              "      <th>usan_year</th>\n",
              "      <th>withdrawn_flag</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>[B01AC06, N02BA01, N02BA51, A01AD05, N02BA71]</td>\n",
              "      <td>2</td>\n",
              "      <td>None</td>\n",
              "      <td>0</td>\n",
              "      <td>15365</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>[{'xref_id': 'aspirin', 'xref_name': 'aspirin'...</td>\n",
              "      <td>True</td>\n",
              "      <td>1950</td>\n",
              "      <td>...</td>\n",
              "      <td>ASPIRIN</td>\n",
              "      <td>0</td>\n",
              "      <td>MOL</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[B01AC06, N02BA01, N02BA51, A01AD05, N02BA71]</td>\n",
              "      <td>2</td>\n",
              "      <td>None</td>\n",
              "      <td>0</td>\n",
              "      <td>15365</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>[{'xref_id': 'aspirin', 'xref_name': 'aspirin'...</td>\n",
              "      <td>True</td>\n",
              "      <td>1950</td>\n",
              "      <td>...</td>\n",
              "      <td>ASPIRIN</td>\n",
              "      <td>0</td>\n",
              "      <td>MOL</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 35 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2c3ed0e8-0426-4184-8aa4-9127ef59456c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-5e77de59-6bb6-4bfa-a734-164ddf28ac2f\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5e77de59-6bb6-4bfa-a734-164ddf28ac2f')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-5e77de59-6bb6-4bfa-a734-164ddf28ac2f button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2c3ed0e8-0426-4184-8aa4-9127ef59456c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2c3ed0e8-0426-4184-8aa4-9127ef59456c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:Quickchart encountered unexpected dtypes in columns: \"(['atc_classifications', 'cross_references', 'molecule_hierarchy', 'molecule_properties', 'molecule_structures', 'molecule_synonyms'],)\"\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No charts were generated by quickchart\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:Quickchart encountered unexpected dtypes in columns: \"(['atc_classifications', 'cross_references', 'molecule_hierarchy', 'molecule_properties', 'molecule_structures', 'molecule_synonyms'],)\"\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No charts were generated by quickchart\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "En el ejemplo anterior se arrojan dos entradas asociadas al input \"aspirin\". Esto regularmente puede pasar debido a alguna de las siguientes razones:\n",
        "\n",
        "1. Duplicados en la base de datos: Es posible que haya múltiples entradas en la base de datos de ChEMBL que correspondan a la etiqueta indicada con información ligeramente diferente. Esto podría deberse a diferentes formas de representar la misma molécula o diferentes registros para diferentes formas o sales de la molécula.\n",
        "2. Filtros de búsqueda: Es importante revisar los filtros de búsqueda que se están aplicando en el código. En el ejemplo, se está buscando la columna \"pref_name\" (nombre preferido) que sea igual a \"aspirin\" sin distinción entre mayúsculas y minúsculas. Es posible que haya múltiples entradas que cumplan con este criterio.\n",
        "3. Datos actualizados: La base de datos de ChEMBL se actualiza constantemente con nueva información y registros. Puede haber diferencias en los resultados si se han realizado actualizaciones o cambios en la base de datos desde la última vez que se realizó la búsqueda.\n",
        "\n",
        "A continuación se revisa la información contenida en la columna del identificador de ChEMBL (\"ChEMBL ID\") con el fin de corroborar la similitud entre la información obtenida.\n",
        "Finalmente, se visualiza la molécula que se encuentra asociada a esos identificadores."
      ],
      "metadata": {
        "id": "7XPnD5da4DR0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualizamos la información contenida en la columna de ChEMBL ID\n",
        "mol_df['molecule_chembl_id']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6x0epzlTI2Mr",
        "outputId": "f8f0ba69-556a-42f0-c2c0-e0562206bb9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    CHEMBL25\n",
              "1    CHEMBL25\n",
              "Name: molecule_chembl_id, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "imagen = new_client.image #Modulo para visualizar imágenes de moléculas\n",
        "imagen.set_format('svg')\n",
        "print('\\033[1;30;47m' + 'Aspirina' + '\\033[0m') #Formato para imprimir el nombre en negritas y sombreado\n",
        "\n",
        "SVG(imagen.get('CHEMBL25'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "sxHDTjd-I5Id",
        "outputId": "7165c210-6204-4ce0-b9f0-81b99e51beb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;47mAspirina\u001b[0m\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
            "image/svg+xml": "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:rdkit=\"http://www.rdkit.org/xml\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" version=\"1.1\" baseProfile=\"full\" xml:space=\"preserve\" width=\"500px\" height=\"500px\" viewBox=\"0 0 500 500\">\n<!-- END OF HEADER -->\n<path class=\"bond-0 atom-0 atom-1\" d=\"M 41.9,282.0 L 41.7,381.7\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-0 atom-0 atom-1\" d=\"M 56.8,290.6 L 56.6,373.1\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-1 atom-1 atom-2\" d=\"M 41.7,381.7 L 127.8,431.4\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-2 atom-2 atom-3\" d=\"M 127.8,431.4 L 214.2,381.6\" 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          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "# **Recursos disponibles a través de la API (*endpoints*)**\n",
        "---\n",
        "\n",
        "La API de ChEMBL nos ofrece una serie de módulos, a través de los cuales se puede obtener una amplia gama de información. Esta información puede ser filtrada empleando una serie de herramientas y palabras clave que se abordarán en las siguientes secciones.\n",
        "\n",
        "Las líneas de código que se encuentran a continuación enlistan los diferentes módulos que se pueden emplear a partir del uso de la API (columna etiquetada como '3'). En el mismo sentido, cada fila representa la estructura en programación para generar la llamada del módulo deseado en python (por ejemplo: `actividad = new_client.activity`)"
      ],
      "metadata": {
        "id": "zOhoSqUfLRQs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "recursos = [recursos for recursos in dir(new_client) if not recursos.startswith('_')]\n",
        "recursos_disponibles = pd.DataFrame({'3': recursos})\n",
        "recursos_disponibles['1']='new_client'\n",
        "recursos_disponibles['2']='.'\n",
        "recursos_disponibles=recursos_disponibles[['1','2','3']]\n",
        "recursos_disponibles"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "E-IlYo2Muv8S",
        "outputId": "005579d3-e52a-4c44-e0d6-f0f3f3b486f6"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "             1  2                                        3\n",
              "0   new_client  .                                 activity\n",
              "1   new_client  .  activity_supplementary_data_by_activity\n",
              "2   new_client  .                                    assay\n",
              "3   new_client  .                              assay_class\n",
              "4   new_client  .                                atc_class\n",
              "5   new_client  .                             binding_site\n",
              "6   new_client  .                           biotherapeutic\n",
              "7   new_client  .                                cell_line\n",
              "8   new_client  .                         chembl_id_lookup\n",
              "9   new_client  .                          compound_record\n",
              "10  new_client  .                compound_structural_alert\n",
              "11  new_client  .                              description\n",
              "12  new_client  .                                 document\n",
              "13  new_client  .                      document_similarity\n",
              "14  new_client  .                                     drug\n",
              "15  new_client  .                          drug_indication\n",
              "16  new_client  .                             drug_warning\n",
              "17  new_client  .                                  go_slim\n",
              "18  new_client  .                                    image\n",
              "19  new_client  .                                mechanism\n",
              "20  new_client  .                               metabolism\n",
              "21  new_client  .                                 molecule\n",
              "22  new_client  .                            molecule_form\n",
              "23  new_client  .                                 official\n",
              "24  new_client  .                                 organism\n",
              "25  new_client  .                   protein_classification\n",
              "26  new_client  .                               similarity\n",
              "27  new_client  .                                   source\n",
              "28  new_client  .                             substructure\n",
              "29  new_client  .                                   target\n",
              "30  new_client  .                         target_component\n",
              "31  new_client  .                          target_relation\n",
              "32  new_client  .                                   tissue\n",
              "33  new_client  .                              xref_source"
            ],
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              "      <th>0</th>\n",
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              "      <th>1</th>\n",
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              "      <td>.</td>\n",
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              "      <th>2</th>\n",
              "      <td>new_client</td>\n",
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              "      <th>3</th>\n",
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              "      <td>new_client</td>\n",
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              "      <td>new_client</td>\n",
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              "      <td>new_client</td>\n",
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              "      <td>.</td>\n",
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              "      <td>new_client</td>\n",
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              "      <td>new_client</td>\n",
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              "      <td>new_client</td>\n",
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              "      <td>new_client</td>\n",
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              "      <td>new_client</td>\n",
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              "      <th>26</th>\n",
              "      <td>new_client</td>\n",
              "      <td>.</td>\n",
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              "      <td>new_client</td>\n",
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              "      <td>new_client</td>\n",
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              "      <td>new_client</td>\n",
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              "      <td>new_client</td>\n",
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              "      <td>new_client</td>\n",
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              "      <td>new_client</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Podemos visualizar que existen 34 módulos diferentes que nos arrojan información particular y enfocada.\n"
      ],
      "metadata": {
        "id": "L0FvJnkWso48"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "# **Filtros disponibles (palabras clave)**\n",
        "---\n",
        "Existe una amplia variedad de filtros para el módulo \"new_client\" que nos permiten afinar las búsquedas a partir de palabras clave, las cuales son:\n",
        "\n",
        "*   ***iexact***. Permite realizar una búsqueda sin distinguir entre mayúsculas y minúsculas.\n",
        "*   ***contains***. Busca objetos que contengan un valor específico.\n",
        " *   ***icontains***. Similar a contains, pero sin distinguir entre mayúsculas y minúsculas.\n",
        "*   ***in***. Busca objetos cuyo valor esté dentro de una lista de valores.\n",
        "\n",
        "---------------------Comparaciones numéricas---------------------\n",
        " * ***gt*** (mayor que)\n",
        "  * ***gte*** (mayor o igual que)\n",
        " * ***lt*** (menor que)\n",
        "  * ***lte*** (menor o igual que).\n",
        "\n",
        "----------------Comparaciones de cadena de texto----------------\n",
        " *   ***startswith***. Comienza con.\n",
        "  *   ***istartswith***. Comienza con, sin distinguir entre mayúsculas y minúsculas.\n",
        " *   ***endswith***. Termina con.\n",
        "  *   ***iendswith***. Termina con, sin distinguir entre mayúsculas y minúsculas.\n",
        "\n",
        "*   ***range***. Permite buscar objetos cuyo valor esté dentro de un rango específico.\n",
        "*   ***isnull***. Permite buscar objetos que tengan un campo nulo (True) o no (False).\n",
        "*   ***regex***.  Permite realizar búsquedas utilizando expresiones regulares.(r'^[A-Za-z]+$', buscará objetos cuyo nombre consiste solo de letras).\n",
        " *   ***iregex***. Similar a regex, pero sin distinguir entre mayúsculas y minúsculas en la búsqueda.\n",
        "----------------------------------------------------------------------\n",
        "Lo anterior se permite debido a que esta API se encuentra construido a partir de Django (https://docs.djangoproject.com/en/1.11/ref/models/querysets).\n",
        "\n",
        "\n",
        "## ***Doble guión bajo: __***\n",
        "Es usado para emplear las palabras claves para afinar la búsqueda.\n",
        "\n",
        "## ***Operador: only***\n",
        "Only es un método que limita los resultados con base en un conjunto seleccionado de campos para una base de datos. Este método toma una lista de campos (como un solo argumento) para ser incluidos en los resultados. Al usar only se hace un llamamiento a la API de forma rápida debido a que se toma una menor cantidad de información.\n",
        "Nota: cualquier campo especificado ignorará los campos anidados (\"logP\" es equivalente a \"propiedades moleculares\")\n"
      ],
      "metadata": {
        "id": "0s39fOS0Ln14"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ····\n"
      ],
      "metadata": {
        "id": "IJPjOSu2Wbqj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "# ***1. Moléculas***\n",
        "---\n",
        "\n",
        "Este modulo (*molecule*) usualmente tienen asociados datos de bioactividad.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "q-LQ5_30QgfK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Búsqueda por:"
      ],
      "metadata": {
        "id": "W8ZsNMFxdt1Q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Nombre exacto***\n",
        "A continuación se realizará la búsqueda a partir del nombre exacto de un fármaco común (acetaminofén).\n"
      ],
      "metadata": {
        "id": "gYUZnYJ1dyBm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   Módulo = ***molecule***\n",
        "*   Palabra clave= ***iexact***\n",
        "\n"
      ],
      "metadata": {
        "id": "Alu_1SqHgWEW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "molecula = new_client.molecule\n",
        "mol = molecula.filter(pref_name__iexact='acetaminophen') #naproxen, aspirin\n",
        "mol_df= pd.DataFrame.from_records(mol)\n",
        "print(mol_df.info())\n",
        "mol_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "YrpB2fxbRMca",
        "outputId": "67b70189-15d5-4bf5-bed0-1ca967c26a2c"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2 entries, 0 to 1\n",
            "Data columns (total 35 columns):\n",
            " #   Column                Non-Null Count  Dtype \n",
            "---  ------                --------------  ----- \n",
            " 0   atc_classifications   2 non-null      object\n",
            " 1   availability_type     2 non-null      int64 \n",
            " 2   biotherapeutic        0 non-null      object\n",
            " 3   black_box_warning     2 non-null      int64 \n",
            " 4   chebi_par_id          2 non-null      int64 \n",
            " 5   chemical_probe        2 non-null      int64 \n",
            " 6   chirality             2 non-null      int64 \n",
            " 7   cross_references      2 non-null      object\n",
            " 8   dosed_ingredient      2 non-null      bool  \n",
            " 9   first_approval        2 non-null      int64 \n",
            " 10  first_in_class        2 non-null      int64 \n",
            " 11  helm_notation         0 non-null      object\n",
            " 12  indication_class      2 non-null      object\n",
            " 13  inorganic_flag        2 non-null      int64 \n",
            " 14  max_phase             2 non-null      object\n",
            " 15  molecule_chembl_id    2 non-null      object\n",
            " 16  molecule_hierarchy    2 non-null      object\n",
            " 17  molecule_properties   2 non-null      object\n",
            " 18  molecule_structures   2 non-null      object\n",
            " 19  molecule_synonyms     2 non-null      object\n",
            " 20  molecule_type         2 non-null      object\n",
            " 21  natural_product       2 non-null      int64 \n",
            " 22  oral                  2 non-null      bool  \n",
            " 23  parenteral            2 non-null      bool  \n",
            " 24  polymer_flag          2 non-null      int64 \n",
            " 25  pref_name             2 non-null      object\n",
            " 26  prodrug               2 non-null      int64 \n",
            " 27  structure_type        2 non-null      object\n",
            " 28  therapeutic_flag      2 non-null      bool  \n",
            " 29  topical               2 non-null      bool  \n",
            " 30  usan_stem             0 non-null      object\n",
            " 31  usan_stem_definition  0 non-null      object\n",
            " 32  usan_substem          0 non-null      object\n",
            " 33  usan_year             0 non-null      object\n",
            " 34  withdrawn_flag        2 non-null      bool  \n",
            "dtypes: bool(6), int64(11), object(18)\n",
            "memory usage: 604.0+ bytes\n",
            "None\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           atc_classifications  availability_type biotherapeutic  \\\n",
              "0  [N02BE51, N02BE01, N02BE71]                  2           None   \n",
              "1  [N02BE51, N02BE01, N02BE71]                  2           None   \n",
              "\n",
              "   black_box_warning  chebi_par_id  chemical_probe  chirality  \\\n",
              "0                  1         46195               0          2   \n",
              "1                  1         46195               0          2   \n",
              "\n",
              "                                    cross_references  dosed_ingredient  \\\n",
              "0  [{'xref_id': 'acetaminophen', 'xref_name': 'ac...              True   \n",
              "1  [{'xref_id': 'acetaminophen', 'xref_name': 'ac...              True   \n",
              "\n",
              "   first_approval  ...      pref_name prodrug structure_type  \\\n",
              "0            1968  ...  ACETAMINOPHEN       1            MOL   \n",
              "1            1968  ...  ACETAMINOPHEN       1            MOL   \n",
              "\n",
              "   therapeutic_flag topical usan_stem usan_stem_definition usan_substem  \\\n",
              "0              True    True      None                 None         None   \n",
              "1              True    True      None                 None         None   \n",
              "\n",
              "  usan_year withdrawn_flag  \n",
              "0      None          False  \n",
              "1      None          False  \n",
              "\n",
              "[2 rows x 35 columns]"
            ],
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
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              "\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-98eb6429-1178-401b-b4a8-620f4ec9de27');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mol_df['molecule_chembl_id']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lTywVjJ9YSNP",
        "outputId": "a482d174-5451-41a3-de6b-0d97c3fb07f8"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    CHEMBL112\n",
              "1    CHEMBL112\n",
              "Name: molecule_chembl_id, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Visualización de moléculas:\n",
        "*   Módulo = ***image***"
      ],
      "metadata": {
        "id": "G9NObIu2Yq-S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "imagen = new_client.image\n",
        "imagen.set_format('svg')\n",
        "print('\\033[1;30;47m' + 'Paracetamol' + '\\033[0m') #Formato de texto de la etiqueta\n",
        "SVG(imagen.get('CHEMBL112'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "qxSB7jGPYqSI",
        "outputId": "164cea68-7533-4e66-9b60-e2fc51ad75b7"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;47mParacetamol\u001b[0m\n"
          ]
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          },
          "metadata": {},
          "execution_count": 28
        }
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    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Sinónimos***\n",
        "En algunos casos es más común conocer a un compuesto por un sinónimo (módulo: *molecule_synonym*) que por su nombre preferido en ChEMBL (módulo: *pref_name*).\n",
        "En este ejemplo realizaremos la búsqueda del acetaminofén a partir de su sinónimo más conocido, **paracetamol**.\n",
        "\n",
        "*   Módulo = ***molecule***\n",
        "*   Palabras clave= ***molecule_synonym***, ***istartswith***\n",
        "*   Operador= ***only***\n",
        "\n"
      ],
      "metadata": {
        "id": "2ZuXwnV1TCkG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mol = molecula.filter(molecule_synonyms__molecule_synonym__istartswith='parac').only('molecule_chembl_id', 'pref_name','molecule_structures', 'molecule_synonyms')\n",
        "                                                                                  #'first_approval','max_phase','oral','parenteral','topical','molecule_properties')\n",
        "#molecule_synonym está anidada en molecule_synonyms.\n",
        "mol_df1= pd.DataFrame.from_records(mol)\n",
        "#Información particular como el smiles canónico o el estándar InchiKey se encuentran anidados en\n",
        "#la columna 'molecule_structures' y con las siguientes líneas es posible extraer esa información.\n",
        "mol_df1['canonical_smiles'] = mol_df1.loc[ mol_df1['molecule_structures'].notnull(), 'molecule_structures'].apply(lambda x: x['canonical_smiles'])\n",
        "mol_df1['standard_inchi_key'] = mol_df1.loc[ mol_df1['molecule_structures'].notnull(), 'molecule_structures'].apply(lambda x: x['standard_inchi_key'])\n",
        "\n",
        "mol_df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "Mozes7a2T3Lu",
        "outputId": "31a06d6a-627f-48aa-c966-a1fc14465b5d"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  molecule_chembl_id                                molecule_structures  \\\n",
              "0          CHEMBL112  {'canonical_smiles': 'CC(=O)Nc1ccc(O)cc1', 'mo...   \n",
              "1          CHEMBL112  {'canonical_smiles': 'CC(=O)Nc1ccc(O)cc1', 'mo...   \n",
              "2          CHEMBL635  {'canonical_smiles': 'C[C@]12C=CC(=O)C=C1CC[C@...   \n",
              "3        CHEMBL57053  {'canonical_smiles': 'Oc1ccc(Cl)cc1', 'molfile...   \n",
              "4         CHEMBL1196  {'canonical_smiles': 'CCCOc1ccc(C(=O)OCCN(CC)C...   \n",
              "5       CHEMBL398440  {'canonical_smiles': 'Cc1cc(O)cc(C)c1Cl', 'mol...   \n",
              "6      CHEMBL1200464  {'canonical_smiles': 'CCCOc1ccc(C(=O)OCCN(CC)C...   \n",
              "7      CHEMBL1410743  {'canonical_smiles': 'CC1OC(C)OC(C)O1', 'molfi...   \n",
              "\n",
              "                                   molecule_synonyms  \\\n",
              "0  [{'molecule_synonym': 'Abdine', 'syn_type': 'T...   \n",
              "1  [{'molecule_synonym': 'Abdine', 'syn_type': 'T...   \n",
              "2  [{'molecule_synonym': '3en3hg4wsw', 'syn_type'...   \n",
              "3  [{'molecule_synonym': 'Parachlorophenol', 'syn...   \n",
              "4  [{'molecule_synonym': 'Alcaine', 'syn_type': '...   \n",
              "5  [{'molecule_synonym': 'Chloroxylenol', 'syn_ty...   \n",
              "6  [{'molecule_synonym': 'Ak-taine', 'syn_type': ...   \n",
              "7  [{'molecule_synonym': '2,4,6-trimethyl-s-triox...   \n",
              "\n",
              "                    pref_name  \\\n",
              "0               ACETAMINOPHEN   \n",
              "1               ACETAMINOPHEN   \n",
              "2                  PREDNISONE   \n",
              "3            PARACHLOROPHENOL   \n",
              "4                PROPARACAINE   \n",
              "5               CHLOROXYLENOL   \n",
              "6  PROPARACAINE HYDROCHLORIDE   \n",
              "7                 PARALDEHYDE   \n",
              "\n",
              "                                    canonical_smiles  \\\n",
              "0                                 CC(=O)Nc1ccc(O)cc1   \n",
              "1                                 CC(=O)Nc1ccc(O)cc1   \n",
              "2  C[C@]12C=CC(=O)C=C1CC[C@@H]1[C@@H]2C(=O)C[C@@]...   \n",
              "3                                      Oc1ccc(Cl)cc1   \n",
              "4                     CCCOc1ccc(C(=O)OCCN(CC)CC)cc1N   \n",
              "5                                  Cc1cc(O)cc(C)c1Cl   \n",
              "6                  CCCOc1ccc(C(=O)OCCN(CC)CC)cc1N.Cl   \n",
              "7                                    CC1OC(C)OC(C)O1   \n",
              "\n",
              "            standard_inchi_key  \n",
              "0  RZVAJINKPMORJF-UHFFFAOYSA-N  \n",
              "1  RZVAJINKPMORJF-UHFFFAOYSA-N  \n",
              "2  XOFYZVNMUHMLCC-ZPOLXVRWSA-N  \n",
              "3  WXNZTHHGJRFXKQ-UHFFFAOYSA-N  \n",
              "4  KCLANYCVBBTKTO-UHFFFAOYSA-N  \n",
              "5  OSDLLIBGSJNGJE-UHFFFAOYSA-N  \n",
              "6  BFUUJUGQJUTPAF-UHFFFAOYSA-N  \n",
              "7  SQYNKIJPMDEDEG-UHFFFAOYSA-N  "
            ],
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              "\n",
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              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>molecule_structures</th>\n",
              "      <th>molecule_synonyms</th>\n",
              "      <th>pref_name</th>\n",
              "      <th>canonical_smiles</th>\n",
              "      <th>standard_inchi_key</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL112</td>\n",
              "      <td>{'canonical_smiles': 'CC(=O)Nc1ccc(O)cc1', 'mo...</td>\n",
              "      <td>[{'molecule_synonym': 'Abdine', 'syn_type': 'T...</td>\n",
              "      <td>ACETAMINOPHEN</td>\n",
              "      <td>CC(=O)Nc1ccc(O)cc1</td>\n",
              "      <td>RZVAJINKPMORJF-UHFFFAOYSA-N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL112</td>\n",
              "      <td>{'canonical_smiles': 'CC(=O)Nc1ccc(O)cc1', 'mo...</td>\n",
              "      <td>[{'molecule_synonym': 'Abdine', 'syn_type': 'T...</td>\n",
              "      <td>ACETAMINOPHEN</td>\n",
              "      <td>CC(=O)Nc1ccc(O)cc1</td>\n",
              "      <td>RZVAJINKPMORJF-UHFFFAOYSA-N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL635</td>\n",
              "      <td>{'canonical_smiles': 'C[C@]12C=CC(=O)C=C1CC[C@...</td>\n",
              "      <td>[{'molecule_synonym': '3en3hg4wsw', 'syn_type'...</td>\n",
              "      <td>PREDNISONE</td>\n",
              "      <td>C[C@]12C=CC(=O)C=C1CC[C@@H]1[C@@H]2C(=O)C[C@@]...</td>\n",
              "      <td>XOFYZVNMUHMLCC-ZPOLXVRWSA-N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL57053</td>\n",
              "      <td>{'canonical_smiles': 'Oc1ccc(Cl)cc1', 'molfile...</td>\n",
              "      <td>[{'molecule_synonym': 'Parachlorophenol', 'syn...</td>\n",
              "      <td>PARACHLOROPHENOL</td>\n",
              "      <td>Oc1ccc(Cl)cc1</td>\n",
              "      <td>WXNZTHHGJRFXKQ-UHFFFAOYSA-N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL1196</td>\n",
              "      <td>{'canonical_smiles': 'CCCOc1ccc(C(=O)OCCN(CC)C...</td>\n",
              "      <td>[{'molecule_synonym': 'Alcaine', 'syn_type': '...</td>\n",
              "      <td>PROPARACAINE</td>\n",
              "      <td>CCCOc1ccc(C(=O)OCCN(CC)CC)cc1N</td>\n",
              "      <td>KCLANYCVBBTKTO-UHFFFAOYSA-N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>CHEMBL398440</td>\n",
              "      <td>{'canonical_smiles': 'Cc1cc(O)cc(C)c1Cl', 'mol...</td>\n",
              "      <td>[{'molecule_synonym': 'Chloroxylenol', 'syn_ty...</td>\n",
              "      <td>CHLOROXYLENOL</td>\n",
              "      <td>Cc1cc(O)cc(C)c1Cl</td>\n",
              "      <td>OSDLLIBGSJNGJE-UHFFFAOYSA-N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>CHEMBL1200464</td>\n",
              "      <td>{'canonical_smiles': 'CCCOc1ccc(C(=O)OCCN(CC)C...</td>\n",
              "      <td>[{'molecule_synonym': 'Ak-taine', 'syn_type': ...</td>\n",
              "      <td>PROPARACAINE HYDROCHLORIDE</td>\n",
              "      <td>CCCOc1ccc(C(=O)OCCN(CC)CC)cc1N.Cl</td>\n",
              "      <td>BFUUJUGQJUTPAF-UHFFFAOYSA-N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>CHEMBL1410743</td>\n",
              "      <td>{'canonical_smiles': 'CC1OC(C)OC(C)O1', 'molfi...</td>\n",
              "      <td>[{'molecule_synonym': '2,4,6-trimethyl-s-triox...</td>\n",
              "      <td>PARALDEHYDE</td>\n",
              "      <td>CC1OC(C)OC(C)O1</td>\n",
              "      <td>SQYNKIJPMDEDEG-UHFFFAOYSA-N</td>\n",
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              "\n",
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              "\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Comparar información obtenida con la información arrojada a través de la interfaz gráfica de ChEMBL ([Clic aquí](https://www.ebi.ac.uk/chembl/compound_report_card/CHEMBL112/))"
      ],
      "metadata": {
        "id": "b_whEEKMaZoq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***ChEMBL ID***\n",
        "*   Módulo = ***molecule***\n",
        "*   Palabra clave= ***in***\n",
        "*   Operador= ***only***\n",
        "\n",
        "ChEMBL ID: identificador estable diseñado para una búsqueda directa de datos ([click aquí](**https**://www.ebi.ac.uk/chembl/)).\n",
        "\n",
        "Está búsqueda se puede realizar a partir de un solo o  múltiples identificadores. Para este ejemplo se consideran los identificadores para la aspirina (CHEMBL25), paracetamol (CHEMBL112) y naproxeno (CHEMBL154).\n"
      ],
      "metadata": {
        "id": "iD3BrXiVUyDZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#mol = molecula.filter(chembl_id='CHEMBL25').only(['molecule_chembl_id', 'pref_name',\n",
        " #                                                  'molecule_structures'])\n",
        "mol = molecula.filter(molecule_chembl_id__in=['CHEMBL25', 'CHEMBL112', 'CHEMBL154',]).only(['molecule_chembl_id', 'pref_name'])\n",
        "mol_df2= pd.DataFrame.from_records(mol)\n",
        "mol_df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "K-aOCrCzVU0C",
        "outputId": "c2058d43-75d8-4316-f5ff-9a4595dd00fb"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  molecule_chembl_id      pref_name\n",
              "0           CHEMBL25        ASPIRIN\n",
              "1           CHEMBL25        ASPIRIN\n",
              "2          CHEMBL112  ACETAMINOPHEN\n",
              "3          CHEMBL154       NAPROXEN"
            ],
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              "      <td>CHEMBL25</td>\n",
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              "    <tr>\n",
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              "      <td>ASPIRIN</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL112</td>\n",
              "      <td>ACETAMINOPHEN</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL154</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-df892099-7cf4-44a7-a386-3af0edf93057');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Inchi Key standar***\n",
        "\n",
        "*   Módulo = ***molecule***\n",
        "*   Palabra clave= ***standard_inchi_key***\n",
        "*   Operador= ***only***"
      ],
      "metadata": {
        "id": "_dklF7HgWGm3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mol = molecula.filter(molecule_structures__standard_inchi_key='RZVAJINKPMORJF-UHFFFAOYSA-N').only(['molecule_chembl_id', 'pref_name', 'molecule_structures'])\n",
        "mol_df3= pd.DataFrame.from_records(mol)\n",
        "mol_df3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "KAejUXdcfKpP",
        "outputId": "47af145e-d137-4673-c489-3d8a68f2cc7f"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  molecule_chembl_id                                molecule_structures  \\\n",
              "0          CHEMBL112  {'canonical_smiles': 'CC(=O)Nc1ccc(O)cc1', 'mo...   \n",
              "1          CHEMBL112  {'canonical_smiles': 'CC(=O)Nc1ccc(O)cc1', 'mo...   \n",
              "\n",
              "       pref_name  \n",
              "0  ACETAMINOPHEN  \n",
              "1  ACETAMINOPHEN  "
            ],
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              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-bfbfa6ea-2a34-4696-96d9-2f09feb82ede button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9eb56e61-cd48-45ec-b544-9252cc4fcc72 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9eb56e61-cd48-45ec-b544-9252cc4fcc72');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***SMILES***\n",
        "*   Módulo = ***molecule***\n",
        "*   Palabra clave= ***canonical_smiles***\n",
        "*   Operador= ***only**"
      ],
      "metadata": {
        "id": "W0k017LCh5uk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mol = molecula.filter(molecule_structures__canonical_smiles='CC(=O)Nc1ccc(O)cc1').only(['molecule_chembl_id', 'pref_name', 'molecule_structures'])\n",
        "mol_df4= pd.DataFrame.from_records(mol)\n",
        "mol_df4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "-7hjMAVsgbl8",
        "outputId": "f0b0784c-5d28-47c5-b242-b4eafe0cc39b"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  molecule_chembl_id                                molecule_structures  \\\n",
              "0          CHEMBL112  {'canonical_smiles': 'CC(=O)Nc1ccc(O)cc1', 'mo...   \n",
              "1          CHEMBL112  {'canonical_smiles': 'CC(=O)Nc1ccc(O)cc1', 'mo...   \n",
              "\n",
              "       pref_name  \n",
              "0  ACETAMINOPHEN  \n",
              "1  ACETAMINOPHEN  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-e32efacd-cf9c-4be0-8fa1-982ea0a8de42\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>molecule_structures</th>\n",
              "      <th>pref_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL112</td>\n",
              "      <td>{'canonical_smiles': 'CC(=O)Nc1ccc(O)cc1', 'mo...</td>\n",
              "      <td>ACETAMINOPHEN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL112</td>\n",
              "      <td>{'canonical_smiles': 'CC(=O)Nc1ccc(O)cc1', 'mo...</td>\n",
              "      <td>ACETAMINOPHEN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e32efacd-cf9c-4be0-8fa1-982ea0a8de42')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-9de05397-8f77-4939-8598-6208bf77771c button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e32efacd-cf9c-4be0-8fa1-982ea0a8de42 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e32efacd-cf9c-4be0-8fa1-982ea0a8de42');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Similitud, considerando:***\n"
      ],
      "metadata": {
        "id": "oSv3vowAhJ_x"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### ***SMILES***\n",
        "*   Módulo = ***similarity*** (métrica empleada: coeficiente de tanimoto)\n",
        "*   Operador= ***only***\n"
      ],
      "metadata": {
        "id": "egbp7p_tD8RP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "similarity = new_client.similarity\n",
        "res = similarity.filter(smiles=\"CC(=O)Nc1ccc(O)cc1\", similarity=70).only(['molecule_chembl_id', 'similarity'])\n",
        "for i in res:\n",
        "    print(i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6AGiC7eLgShu",
        "outputId": "ac3e4390-0c99-462c-dbd3-b0d72322f897"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'molecule_chembl_id': 'CHEMBL112', 'similarity': '100'}\n",
            "{'molecule_chembl_id': 'CHEMBL1360012', 'similarity': '76.9230782985687255859375'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "imagen.set_format('svg')\n",
        "SVG(imagen.get('CHEMBL1360012'))\n",
        "#SVG(image.get('CHEMBL112'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "DVwLu7S7Bfuv",
        "outputId": "20476a94-5fb5-487a-d0b1-30ce398cad00"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
            "image/svg+xml": "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:rdkit=\"http://www.rdkit.org/xml\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" version=\"1.1\" baseProfile=\"full\" xml:space=\"preserve\" width=\"500px\" height=\"500px\" viewBox=\"0 0 500 500\">\n<!-- END OF HEADER -->\n<path class=\"bond-0 atom-0 atom-14\" d=\"M 231.3,47.0 L 231.3,62.1\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-0 atom-0 atom-14\" d=\"M 231.3,62.1 L 231.3,77.1\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-1 atom-1 atom-17\" d=\"M 228.1,453.0 L 228.1,436.8\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-1 atom-1 atom-17\" d=\"M 228.1,436.8 L 228.1,420.6\" 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style=\"fill:none;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;\"/>\n<path d=\"M 268.7,100.9 L 268.7,98.7 L 266.8,97.6\" style=\"fill:none;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;\"/>\n<path d=\"M 232.0,422.1 L 231.3,422.5 L 229.4,421.4\" style=\"fill:none;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;\"/>\n<path class=\"atom-0\" d=\"M 221.1 34.0 Q 221.1 28.7, 223.7 25.7 Q 226.4 22.7, 231.3 22.7 Q 236.2 22.7, 238.9 25.7 Q 241.5 28.7, 241.5 34.0 Q 241.5 39.4, 238.8 42.5 Q 236.2 45.6, 231.3 45.6 Q 226.4 45.6, 223.7 42.5 Q 221.1 39.5, 221.1 34.0 M 231.3 43.0 Q 234.7 43.0, 236.5 40.8 Q 238.4 38.5, 238.4 34.0 Q 238.4 29.7, 236.5 27.5 Q 234.7 25.2, 231.3 25.2 Q 227.9 25.2, 226.1 27.4 Q 224.2 29.6, 224.2 34.0 Q 224.2 38.5, 226.1 40.8 Q 227.9 43.0, 231.3 43.0 \" fill=\"#FF0000\"/>\n<path class=\"atom-0\" d=\"M 245.0 23.0 L 248.0 23.0 L 248.0 32.4 L 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          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### ***ChEMBL ID***"
      ],
      "metadata": {
        "id": "VgAksbqMAorc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   Módulo = ***similarity***\n",
        "*   Operador= ***only***\n"
      ],
      "metadata": {
        "id": "Ut3v9ArmkKTZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "res = similarity.filter(chembl_id='CHEMBL1360012',\n",
        "                        similarity=70).only(['molecule_chembl_id', 'pref_name', 'similarity'])\n",
        "for i in res:\n",
        "    print(i)"
      ],
      "metadata": {
        "id": "nZSDAU5CAuiw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "95e1a830-dc2a-4529-ee1e-3cc9abd5e77a"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'molecule_chembl_id': 'CHEMBL112', 'pref_name': 'ACETAMINOPHEN', 'similarity': '76.9230782985687255859375'}\n",
            "{'molecule_chembl_id': 'CHEMBL1483163', 'pref_name': None, 'similarity': '73.33333492279052734375'}\n",
            "{'molecule_chembl_id': 'CHEMBL311003', 'pref_name': None, 'similarity': '73.33333492279052734375'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "imagen.set_format('svg')\n",
        "SVG(imagen.get('CHEMBL1483163'))\n",
        "#SVG(image.get('CHEMBL112'))"
      ],
      "metadata": {
        "id": "KkJPFWuJk8c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "outputId": "5d0168f1-8478-4132-810e-ac8a47a08bde"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
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          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Conectividad***\n",
        "Las siguientes líneas de código identifican moléculas que tengan una conectividad similar a una representación molecular (SMILES) de referencia.\n",
        "*   Módulo = ***molecule***\n",
        "*   Palabra clave= ***canonical_smiles***, ***connectivity***\n",
        "*   Operador= ***only***"
      ],
      "metadata": {
        "id": "nyUeTFTzCRyi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "res = molecula.filter(molecule_structures__canonical_smiles__connectivity='CC(=O)NC1=CC=C(C=C1)').only(['molecule_chembl_id', 'pref_name', 'molecule_structures'])\n",
        "mol_df5= pd.DataFrame.from_records(res)\n",
        "mol_df5['canonical_smiles'] = mol_df5.loc[ mol_df5['molecule_structures'].notnull(), 'molecule_structures'].apply(lambda x: x['canonical_smiles'])\n",
        "mol_df5"
      ],
      "metadata": {
        "id": "2riSjy0nCWsz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "ec492358-1514-4473-eb02-4d1818faaaa7"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  molecule_chembl_id                                molecule_structures  \\\n",
              "0       CHEMBL269644  {'canonical_smiles': 'CC(=O)Nc1ccccc1', 'molfi...   \n",
              "1       CHEMBL269644  {'canonical_smiles': 'CC(=O)Nc1ccccc1', 'molfi...   \n",
              "2      CHEMBL3349336  {'canonical_smiles': 'CC(=O)Nc1ccccc1.O=S(=O)(...   \n",
              "\n",
              "     pref_name             canonical_smiles  \n",
              "0  ACETANILIDE              CC(=O)Nc1ccccc1  \n",
              "1  ACETANILIDE              CC(=O)Nc1ccccc1  \n",
              "2         None  CC(=O)Nc1ccccc1.O=S(=O)(O)O  "
            ],
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              "      <th>molecule_chembl_id</th>\n",
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              "      <th>0</th>\n",
              "      <td>CHEMBL269644</td>\n",
              "      <td>{'canonical_smiles': 'CC(=O)Nc1ccccc1', 'molfi...</td>\n",
              "      <td>ACETANILIDE</td>\n",
              "      <td>CC(=O)Nc1ccccc1</td>\n",
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              "      <th>1</th>\n",
              "      <td>CHEMBL269644</td>\n",
              "      <td>{'canonical_smiles': 'CC(=O)Nc1ccccc1', 'molfi...</td>\n",
              "      <td>ACETANILIDE</td>\n",
              "      <td>CC(=O)Nc1ccccc1</td>\n",
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              "      <th>2</th>\n",
              "      <td>CHEMBL3349336</td>\n",
              "      <td>{'canonical_smiles': 'CC(=O)Nc1ccccc1.O=S(=O)(...</td>\n",
              "      <td>None</td>\n",
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              "                                                     [key], {});\n",
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        "imagen.set_format('svg')\n",
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        "#SVG(image.get('CHEMBL112'))"
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25.9,285.9 L 23.2,290.5 L 25.8,295.2\" style=\"fill:none;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;\"/>\n<path class=\"atom-1\" d=\"M 307.9 276.4 L 317.2 291.4 Q 318.1 292.9, 319.6 295.5 Q 321.1 298.2, 321.1 298.4 L 321.1 276.4 L 324.9 276.4 L 324.9 304.7 L 321.0 304.7 L 311.1 288.3 Q 309.9 286.4, 308.7 284.2 Q 307.5 282.0, 307.1 281.3 L 307.1 304.7 L 303.4 304.7 L 303.4 276.4 L 307.9 276.4 \" fill=\"#0000FF\"/>\n<path class=\"atom-1\" d=\"M 303.1 307.5 L 306.9 307.5 L 306.9 319.6 L 321.4 319.6 L 321.4 307.5 L 325.2 307.5 L 325.2 335.8 L 321.4 335.8 L 321.4 322.8 L 306.9 322.8 L 306.9 335.8 L 303.1 335.8 L 303.1 307.5 \" fill=\"#0000FF\"/>\n<path class=\"atom-2\" d=\"M 450.8 206.9 Q 450.8 200.1, 454.2 196.3 Q 457.5 192.5, 463.8 192.5 Q 470.1 192.5, 473.5 196.3 Q 476.8 200.1, 476.8 206.9 Q 476.8 213.8, 473.4 217.7 Q 470.0 221.6, 463.8 221.6 Q 457.6 221.6, 454.2 217.7 Q 450.8 213.8, 450.8 206.9 M 463.8 218.4 Q 468.1 218.4, 470.5 215.5 Q 472.8 212.6, 472.8 206.9 Q 472.8 201.3, 470.5 198.5 Q 468.1 195.7, 463.8 195.7 Q 459.5 195.7, 457.1 198.5 Q 454.8 201.3, 454.8 206.9 Q 454.8 212.6, 457.1 215.5 Q 459.5 218.4, 463.8 218.4 \" fill=\"#FF0000\"/>\n</svg>"
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "imagen.set_format('svg')\n",
        "SVG(imagen.get('CHEMBL3349336'))\n",
        "#SVG(image.get('CHEMBL112'))"
      ],
      "metadata": {
        "id": "ELfP1-Aj0WPP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "outputId": "5543d883-9dca-417b-d182-1caa293aae3b"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
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          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Uso de Filtros***"
      ],
      "metadata": {
        "id": "ja3TJgplDEN0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Compuestos bioterapéuticos***\n",
        "Con las siguientes líneas de código se buscarán todas las moléculas bioterapeuticas.\n",
        "*   Módulo = ***molecule***\n",
        "*   Palabra clave= ***isnull***"
      ],
      "metadata": {
        "id": "fPjBk-rNLprz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bioterapeuticos = molecula.filter(biotherapeutic__isnull=False)\n",
        "len(bioterapeuticos)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k2XUXScADJC-",
        "outputId": "dea6b348-fc85-48f4-cbcd-9c0062c86071"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23047"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***MW<300***\n",
        "El filtro empleado a continuación solo selecciona aquellas moléculas que tienen un peso moleculer menor a 300 kDa."
      ],
      "metadata": {
        "id": "XLV6sPGkIhJI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   Módulo= ***molecule***\n",
        "*   Palabra clave= ***mw_freebase***, ***lte***"
      ],
      "metadata": {
        "id": "mUP8C3XhI6R4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "moleculas_ligeras = molecula.filter(molecule_properties__mw_freebase__lte=300)\n",
        "len(moleculas_ligeras)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zSsSzds0HU3a",
        "outputId": "f87d758a-07e3-49df-8f22-d59c6957d6ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "423016"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Filtros anidados***\n"
      ],
      "metadata": {
        "id": "6kAJ2gL2lNwX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Puedes usar varios filtros anidados separados por comas:\n",
        "\n",
        "*   Módulo = ***molecule***\n",
        "*   Palabra clave= ***mw_freebase***, ***lte***,  ***pref_name***, ***iendswith***\n",
        "*   Operador= ***only***\n"
      ],
      "metadata": {
        "id": "p_yBpxgUJnHN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#En las siguientes líneas se buscan moléculas que contengan en su nombre preferido la terminación \"INE\" y un MW<300\n",
        "moleculas_ligeras_nib = molecula.filter(molecule_properties__mw_freebase__lte=300,\n",
        "                                        pref_name__iendswith=\"ine\").only(['molecule_chembl_id', 'pref_name'])\n",
        "mol_df6= pd.DataFrame.from_records(moleculas_ligeras_nib)\n",
        "mol_df6=mol_df6.sort_values(by='pref_name').reset_index(inplace=False)\n",
        "mol_df6[0:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "Q9w9rBKQJl7J",
        "outputId": "055fea9d-57dc-450d-a81a-b41174567ef5"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   index molecule_chembl_id              pref_name\n",
              "0    353       CHEMBL298517        (+)-EPIBATIDINE\n",
              "1    870       CHEMBL501778         (+)-HIGENAMINE\n",
              "2    371        CHEMBL60542        (+)-PENTAZOCINE\n",
              "3    415        CHEMBL76640      (+)-PHYSOSTIGMINE\n",
              "4    805       CHEMBL257990    (+)-TRANYLCYPROMINE\n",
              "5   1039       CHEMBL535389  (+/-)-HOMOEPIBATIDINE\n",
              "6   1796      CHEMBL2414990       (+/-)-NUCIFERINE\n",
              "7   1795      CHEMBL2414988     (+/-)-ROEMREFIDINE\n",
              "8    177       CHEMBL440464          (+/-)NICOTINE\n",
              "9   1718      CHEMBL2236571      (-)-(S)-EDULININE"
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              "      <td>371</td>\n",
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              "    </tr>\n",
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              "      <th>3</th>\n",
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              "      <th>5</th>\n",
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              "      <td>1796</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1795</td>\n",
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              "      <td>(+/-)-ROEMREFIDINE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>177</td>\n",
              "      <td>CHEMBL440464</td>\n",
              "      <td>(+/-)NICOTINE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1718</td>\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
          "execution_count": 45
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mol_df6"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "blueOFU2XJmq",
        "outputId": "12b86102-cfe1-4ee2-f0a4-65e94acb79bf"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      index molecule_chembl_id               pref_name\n",
              "0       353       CHEMBL298517         (+)-EPIBATIDINE\n",
              "1       870       CHEMBL501778          (+)-HIGENAMINE\n",
              "2       371        CHEMBL60542         (+)-PENTAZOCINE\n",
              "3       415        CHEMBL76640       (+)-PHYSOSTIGMINE\n",
              "4       805       CHEMBL257990     (+)-TRANYLCYPROMINE\n",
              "...     ...                ...                     ...\n",
              "1853   1776      CHEMBL2311152      [3H]NOREPINEPHRINE\n",
              "1854   1757      CHEMBL2281472   bishomocubyl-10-amine\n",
              "1855   1723      CHEMBL2251847  carbocyclic Nebularine\n",
              "1856      6         CHEMBL6368       p-IODOAMPHETAMINE\n",
              "1857    977       CHEMBL489553           rel-RITODRINE\n",
              "\n",
              "[1858 rows x 3 columns]"
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              "      <th>1</th>\n",
              "      <td>870</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <th>3</th>\n",
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              "      <th>4</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>1853</th>\n",
              "      <td>1776</td>\n",
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              "    <tr>\n",
              "      <th>1854</th>\n",
              "      <td>1757</td>\n",
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              "      <td>bishomocubyl-10-amine</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1855</th>\n",
              "      <td>1723</td>\n",
              "      <td>CHEMBL2251847</td>\n",
              "      <td>carbocyclic Nebularine</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1856</th>\n",
              "      <td>6</td>\n",
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              "      <td>977</td>\n",
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              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-1e70f35e-70f2-4301-a1af-71593173d532 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b3e86d9a-0b31-4c26-886b-2fc332eb1c9b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b3e86d9a-0b31-4c26-886b-2fc332eb1c9b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Cumplimiento de guías de interés farmacéutico (Lipinski y Veber)***\n",
        "*   Módulo = ***molecule***\n",
        "*   Palabra clave= ***num_ro5_violations***\n"
      ],
      "metadata": {
        "id": "miGO_HULL1T4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sin_violaciones = molecula.filter(molecule_properties__num_ro5_violations=0)\n",
        "len(sin_violaciones)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cUO-MRufHTAK",
        "outputId": "85a9214c-ea73-408e-cb9b-257cf485e9eb"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1655867"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "# ***2. Fármacos***\n",
        "---\n",
        "En contraste con los compuestos, los fármacos (fase 4, max_phase=4) y los candidatos clínicos (fase 1, 2 y 3) en ChEMBL normalmente tienen información curada y no tienen que tener necesariamente mediciones de bioactividad asociada.\n",
        "Los datos en la API drugs se agrega dentro del parentesco del fármaco.\n",
        "\n",
        "*   Módulo= ***drug***\n",
        "*   Operador= ***only***\n"
      ],
      "metadata": {
        "id": "qVdjTNIpMBXU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Obtención de todos los fármacos***"
      ],
      "metadata": {
        "id": "W38NxD_J4YbH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "farmaco = new_client.drug\n",
        "res = farmaco.only(['development_phase','first_approval','molecule_chembl_id',\n",
        "                    'synonyms',  'usan_stem','usan_stem_definition',  'usan_year'])\n",
        "farmaco_df1= pd.DataFrame.from_records(res)\n",
        "farmaco_df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 852
        },
        "id": "FsXq-1byboXW",
        "outputId": "91842c26-aae4-421f-f2f8-f79e4c8ef0a9"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      applicants atc_code_description development_phase  first_approval  \\\n",
              "0           None                 None               4.0          1976.0   \n",
              "1           None                 None               4.0          1976.0   \n",
              "2           None                 None               4.0          1984.0   \n",
              "3           None                 None               4.0          1990.0   \n",
              "4           None                 None               4.0          1964.0   \n",
              "...          ...                  ...               ...             ...   \n",
              "14801       None                 None               2.0             NaN   \n",
              "14802       None                 None              None             NaN   \n",
              "14803       None                 None              None             NaN   \n",
              "14804       None                 None              None             NaN   \n",
              "14805       None                 None              None             NaN   \n",
              "\n",
              "      molecule_chembl_id research_codes  \\\n",
              "0                CHEMBL2           None   \n",
              "1                CHEMBL2           None   \n",
              "2                CHEMBL3           None   \n",
              "3                CHEMBL4           None   \n",
              "4                CHEMBL5           None   \n",
              "...                  ...            ...   \n",
              "14801      CHEMBL5095509           None   \n",
              "14802      CHEMBL5219790           None   \n",
              "14803      CHEMBL5219942           None   \n",
              "14804      CHEMBL5219994           None   \n",
              "14805      CHEMBL5219995           None   \n",
              "\n",
              "                                                synonyms usan_stem  \\\n",
              "0      [Prazosin hydrochloride (FDA, JAN, MI, USAN, U...   -azosin   \n",
              "1      [Prazosin hydrochloride (FDA, JAN, MI, USAN, U...   -azosin   \n",
              "2      [Nicotine bitartrate (MI, USAN), Nicotine bita...      None   \n",
              "3      [Ofloxacin (BAN, FDA, INN, JAN, MI, USAN, USP), ]   -oxacin   \n",
              "4      [Nalidixate sodium (USAN), Nalidixic acid (BAN...      nal-   \n",
              "...                                                  ...       ...   \n",
              "14801                                          [Ddp-225]      None   \n",
              "14802                                        [Cetrimide]      None   \n",
              "14803                                        [Cetrimide]      None   \n",
              "14804                                      [Acriflavine]      None   \n",
              "14805                        [Acriflavine hydrochloride]      None   \n",
              "\n",
              "                                   usan_stem_definition  usan_year  \n",
              "0                     antihypertensives (prazosin type)     1968.0  \n",
              "1                     antihypertensives (prazosin type)     1968.0  \n",
              "2                                                  None     1985.0  \n",
              "3                antibacterials (quinolone derivatives)     1984.0  \n",
              "4      narcotic agonists/antagonists (normorphine type)     1962.0  \n",
              "...                                                 ...        ...  \n",
              "14801                                              None        NaN  \n",
              "14802                                              None        NaN  \n",
              "14803                                              None        NaN  \n",
              "14804                                              None        NaN  \n",
              "14805                                              None        NaN  \n",
              "\n",
              "[14806 rows x 10 columns]"
            ],
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              "\n",
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>applicants</th>\n",
              "      <th>atc_code_description</th>\n",
              "      <th>development_phase</th>\n",
              "      <th>first_approval</th>\n",
              "      <th>molecule_chembl_id</th>\n",
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              "      <th>synonyms</th>\n",
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              "      <th>usan_year</th>\n",
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              "      <th>0</th>\n",
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              "      <td>1976.0</td>\n",
              "      <td>CHEMBL2</td>\n",
              "      <td>None</td>\n",
              "      <td>[Prazosin hydrochloride (FDA, JAN, MI, USAN, U...</td>\n",
              "      <td>-azosin</td>\n",
              "      <td>antihypertensives (prazosin type)</td>\n",
              "      <td>1968.0</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1976.0</td>\n",
              "      <td>CHEMBL2</td>\n",
              "      <td>None</td>\n",
              "      <td>[Prazosin hydrochloride (FDA, JAN, MI, USAN, U...</td>\n",
              "      <td>-azosin</td>\n",
              "      <td>antihypertensives (prazosin type)</td>\n",
              "      <td>1968.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1984.0</td>\n",
              "      <td>CHEMBL3</td>\n",
              "      <td>None</td>\n",
              "      <td>[Nicotine bitartrate (MI, USAN), Nicotine bita...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>1985.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1990.0</td>\n",
              "      <td>CHEMBL4</td>\n",
              "      <td>None</td>\n",
              "      <td>[Ofloxacin (BAN, FDA, INN, JAN, MI, USAN, USP), ]</td>\n",
              "      <td>-oxacin</td>\n",
              "      <td>antibacterials (quinolone derivatives)</td>\n",
              "      <td>1984.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1964.0</td>\n",
              "      <td>CHEMBL5</td>\n",
              "      <td>None</td>\n",
              "      <td>[Nalidixate sodium (USAN), Nalidixic acid (BAN...</td>\n",
              "      <td>nal-</td>\n",
              "      <td>narcotic agonists/antagonists (normorphine type)</td>\n",
              "      <td>1962.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14801</th>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CHEMBL5095509</td>\n",
              "      <td>None</td>\n",
              "      <td>[Ddp-225]</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14802</th>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CHEMBL5219790</td>\n",
              "      <td>None</td>\n",
              "      <td>[Cetrimide]</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14803</th>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CHEMBL5219942</td>\n",
              "      <td>None</td>\n",
              "      <td>[Cetrimide]</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14804</th>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CHEMBL5219994</td>\n",
              "      <td>None</td>\n",
              "      <td>[Acriflavine]</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14805</th>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
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              "      <td>CHEMBL5219995</td>\n",
              "      <td>None</td>\n",
              "      <td>[Acriflavine hydrochloride]</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>14806 rows × 10 columns</p>\n",
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              "      </button>\n",
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              "<style>\n",
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              "    fill: #1967D2;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "\n",
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              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7231dd8f-d88d-42da-aa00-e8fbeee97d2a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7231dd8f-d88d-42da-aa00-e8fbeee97d2a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Empleo de filtros***\n",
        "A partir de la búsqueda anterior se considera la aplicación de filtros.\n",
        "*   Módulo = ***molecule***\n",
        "*   Palabra clave= ***mw_freebase***, ***lte***,  ***pref_name***, ***iendswith***\n",
        "*   Operador= ***only***"
      ],
      "metadata": {
        "id": "8NGMTmdnet7l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### ***Año de liberación, sinónimos y palabras clave de los sinónimos***"
      ],
      "metadata": {
        "id": "WPp1ntyroXQA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "res = farmaco.filter(first_approval__gte=1980).filter(usan_stem=\"-azosin\").only(['development_phase',\n",
        "                                                                                 'first_approval','molecule_chembl_id',\n",
        "                                                                                 'synonyms','usan_stem',\n",
        "                                                                                 'usan_stem_definition','usan_year'])\n",
        "farmaco_df2= pd.DataFrame.from_records(res)\n",
        "farmaco_df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "id": "ODPhUfdAMGZg",
        "outputId": "d30ffe36-699f-4865-8f3a-b742bdc073d2"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  applicants atc_code_description development_phase  first_approval  \\\n",
              "0       None                 None               4.0            1987   \n",
              "1       None                 None               4.0            1987   \n",
              "2       None                 None               4.0            1990   \n",
              "\n",
              "  molecule_chembl_id research_codes  \\\n",
              "0          CHEMBL611           None   \n",
              "1          CHEMBL611           None   \n",
              "2          CHEMBL707           None   \n",
              "\n",
              "                                            synonyms usan_stem  \\\n",
              "0  [Terazosin (BAN, INN, MI), Terazosin hydrochlo...   -azosin   \n",
              "1  [Terazosin (BAN, INN, MI), Terazosin hydrochlo...   -azosin   \n",
              "2  [Doxazosin mesilate (JAN), Doxazosin mesylate ...   -azosin   \n",
              "\n",
              "                usan_stem_definition  usan_year  \n",
              "0  antihypertensives (prazosin type)       1980  \n",
              "1  antihypertensives (prazosin type)       1980  \n",
              "2  antihypertensives (prazosin type)       1981  "
            ],
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              "        displayQuickchartButton(document);\n",
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              "      flex-wrap:wrap;\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
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              "    }\n",
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              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0a22e8a2-56c0-4a25-93a4-9e3cb5af68ff button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0a22e8a2-56c0-4a25-93a4-9e3cb5af68ff');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### ***Fase clínica máxima, clasificación ATC y año de aprobación***\n",
        "A continuación se considera la búsqueda de fármacos aprobados teniendo en cuenta la fase clínica máxima 4, la denominación ATC para inmunosupresores y el año de aprobación correspondiente.\n",
        "*   Módulo = ***molecule***\n",
        "*   Palabra clave= ***mw_freebase, lte, pref_name, iendswith***\n",
        "*   Operador= ***only***\n"
      ],
      "metadata": {
        "id": "-P5_MHzEod2d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "farmacos_aprobados = molecula.filter(max_phase=4, #solo fármacos aprobados\n",
        "                                atc_classifications__level2='L04', #Clasificación ATC de inmunosupresores\n",
        "                                first_approval__gte=2022 #Aprobados despues del 2020 )\n",
        "                                ).order_by('first_approval').only(['atc_classifications','first_approval','indication_class','max_phase','pref_name','molecule_type', 'molecule_chembl_id'])\n",
        "farmaco_df2= pd.DataFrame.from_records(farmacos_aprobados)\n",
        "farmaco_df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "f6fXEe5vcRjg",
        "outputId": "09c260be-116b-41c4-8b65-180a3e767b9a"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  atc_classifications  first_approval indication_class max_phase  \\\n",
              "0           [L04AA55]            2022             None       4.0   \n",
              "1           [L04AA55]            2022             None       4.0   \n",
              "2           [L04AC22]            2022             None       4.0   \n",
              "3           [L04AA56]            2022             None       4.0   \n",
              "\n",
              "  molecule_chembl_id   molecule_type        pref_name  \n",
              "0      CHEMBL4297832        Antibody       SUTIMLIMAB  \n",
              "1      CHEMBL4297832        Antibody       SUTIMLIMAB  \n",
              "2      CHEMBL4297911        Antibody       SPESOLIMAB  \n",
              "3      CHEMBL4596392  Small molecule  DEUCRAVACITINIB  "
            ],
            "text/html": [
              "\n",
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              "      <th>atc_classifications</th>\n",
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              "      <th>0</th>\n",
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              "      <th>1</th>\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-b802392b-8931-4841-82a3-d5da8ead61d2\">\n",
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              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-b802392b-8931-4841-82a3-d5da8ead61d2 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0b1f3801-42bf-4c8e-84ed-fb481415a28e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0b1f3801-42bf-4c8e-84ed-fb481415a28e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Identificación de:***\n"
      ],
      "metadata": {
        "id": "WaXQySqytp4R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### ***Moléculas considerando enfermedades***\n",
        "A continuación se considera la búsqueda de fármacos que se encuentren en fase clínica 3 para una enfermedad particular, en este caso se considera al cáncer de pulmón.\n",
        "*   Módulo = ***drug***\n",
        "*   Palabra clave= ***indication***\n",
        "*   Operador= ***only***"
      ],
      "metadata": {
        "id": "HuFUZt7MfDmr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "indicacion_farmaceutica = new_client.drug_indication\n",
        "cancer_pulmon_indicacion = indicacion_farmaceutica.filter(efo_term__icontains=\"LUNG CARCINOMA\", max_phase_for_ind = 3)\n",
        "cancer_pulmon_fase3 = molecula.filter(molecule_chembl_id__in=[x['molecule_chembl_id'] for x in cancer_pulmon_indicacion]).only(['molecule_chembl_id','molecule_properties'])\n",
        "cancer_pulmon_fase3 = pd.DataFrame(cancer_pulmon_fase3)\n",
        "\n",
        "#Las siguientes líneas desglosan las propiedades fisicoquímicas de interés, de las moléculas que cumplen con los filtros previos.\n",
        "cancer_pulmon_fase3['mw_freebase'] = cancer_pulmon_fase3.loc[ cancer_pulmon_fase3['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['mw_freebase'])\n",
        "cancer_pulmon_fase3['alogp'] = cancer_pulmon_fase3.loc[ cancer_pulmon_fase3['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['alogp'])\n",
        "cancer_pulmon_fase3['aromatic_rings'] = cancer_pulmon_fase3.loc[ cancer_pulmon_fase3['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['aromatic_rings'])\n",
        "cancer_pulmon_fase3['full_molformula'] = cancer_pulmon_fase3.loc[ cancer_pulmon_fase3['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['full_molformula'])\n",
        "cancer_pulmon_fase3['hba'] = cancer_pulmon_fase3.loc[ cancer_pulmon_fase3['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['hba'])\n",
        "cancer_pulmon_fase3['hbd'] = cancer_pulmon_fase3.loc[ cancer_pulmon_fase3['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['hbd'])\n",
        "cancer_pulmon_fase3['heavy_atoms'] = cancer_pulmon_fase3.loc[ cancer_pulmon_fase3['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['heavy_atoms'])\n",
        "cancer_pulmon_fase3['molecular_species'] = cancer_pulmon_fase3.loc[ cancer_pulmon_fase3['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['molecular_species'])\n",
        "cancer_pulmon_fase3['num_lipinski_ro5_violations'] = cancer_pulmon_fase3.loc[ cancer_pulmon_fase3['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['num_lipinski_ro5_violations'])\n",
        "cancer_pulmon_fase3['psa'] = cancer_pulmon_fase3.loc[ cancer_pulmon_fase3['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['psa'])\n",
        "cancer_pulmon_fase3['rtb'] = cancer_pulmon_fase3.loc[ cancer_pulmon_fase3['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['rtb'])\n",
        "\n",
        "cancer_pulmon_fase3.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "FtLp_V_VhGMF",
        "outputId": "10509210-3388-458f-d5b0-2def8ab3560e"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  molecule_chembl_id                                molecule_properties  \\\n",
              "0           CHEMBL25  {'alogp': '1.31', 'aromatic_rings': 1, 'cx_log...   \n",
              "1           CHEMBL38  {'alogp': '5.60', 'aromatic_rings': 0, 'cx_log...   \n",
              "2           CHEMBL45  {'alogp': '1.86', 'aromatic_rings': 2, 'cx_log...   \n",
              "3          CHEMBL481  {'alogp': '4.09', 'aromatic_rings': 3, 'cx_log...   \n",
              "4           CHEMBL84  {'alogp': '1.85', 'aromatic_rings': 3, 'cx_log...   \n",
              "\n",
              "  mw_freebase alogp  aromatic_rings full_molformula  hba  hbd  heavy_atoms  \\\n",
              "0      180.16  1.31             1.0          C9H8O4  3.0  1.0         13.0   \n",
              "1      300.44  5.60             0.0        C20H28O2  1.0  1.0         22.0   \n",
              "2      232.28  1.86             2.0      C13H16N2O2  2.0  2.0         17.0   \n",
              "3      586.69  4.09             3.0      C33H38N4O6  9.0  1.0         43.0   \n",
              "4      421.45  1.85             3.0      C23H23N3O5  8.0  2.0         31.0   \n",
              "\n",
              "  molecular_species  num_lipinski_ro5_violations     psa  rtb  \n",
              "0              ACID                          0.0   63.60  2.0  \n",
              "1              ACID                          1.0   37.30  5.0  \n",
              "2           NEUTRAL                          0.0   54.12  4.0  \n",
              "3              BASE                          1.0  114.20  4.0  \n",
              "4              BASE                          0.0  104.89  3.0  "
            ],
            "text/html": [
              "\n",
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>molecule_properties</th>\n",
              "      <th>mw_freebase</th>\n",
              "      <th>alogp</th>\n",
              "      <th>aromatic_rings</th>\n",
              "      <th>full_molformula</th>\n",
              "      <th>hba</th>\n",
              "      <th>hbd</th>\n",
              "      <th>heavy_atoms</th>\n",
              "      <th>molecular_species</th>\n",
              "      <th>num_lipinski_ro5_violations</th>\n",
              "      <th>psa</th>\n",
              "      <th>rtb</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL25</td>\n",
              "      <td>{'alogp': '1.31', 'aromatic_rings': 1, 'cx_log...</td>\n",
              "      <td>180.16</td>\n",
              "      <td>1.31</td>\n",
              "      <td>1.0</td>\n",
              "      <td>C9H8O4</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>ACID</td>\n",
              "      <td>0.0</td>\n",
              "      <td>63.60</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL38</td>\n",
              "      <td>{'alogp': '5.60', 'aromatic_rings': 0, 'cx_log...</td>\n",
              "      <td>300.44</td>\n",
              "      <td>5.60</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C20H28O2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>ACID</td>\n",
              "      <td>1.0</td>\n",
              "      <td>37.30</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL45</td>\n",
              "      <td>{'alogp': '1.86', 'aromatic_rings': 2, 'cx_log...</td>\n",
              "      <td>232.28</td>\n",
              "      <td>1.86</td>\n",
              "      <td>2.0</td>\n",
              "      <td>C13H16N2O2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>NEUTRAL</td>\n",
              "      <td>0.0</td>\n",
              "      <td>54.12</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL481</td>\n",
              "      <td>{'alogp': '4.09', 'aromatic_rings': 3, 'cx_log...</td>\n",
              "      <td>586.69</td>\n",
              "      <td>4.09</td>\n",
              "      <td>3.0</td>\n",
              "      <td>C33H38N4O6</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>BASE</td>\n",
              "      <td>1.0</td>\n",
              "      <td>114.20</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL84</td>\n",
              "      <td>{'alogp': '1.85', 'aromatic_rings': 3, 'cx_log...</td>\n",
              "      <td>421.45</td>\n",
              "      <td>1.85</td>\n",
              "      <td>3.0</td>\n",
              "      <td>C23H23N3O5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>BASE</td>\n",
              "      <td>0.0</td>\n",
              "      <td>104.89</td>\n",
              "      <td>3.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2e4d5061-81f1-470b-91c5-0e11d54ff4aa button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2e4d5061-81f1-470b-91c5-0e11d54ff4aa');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Las propiedades que pueden ser extraídas son las siguientes:\n",
        "properties = cancer_pulmon_fase3['molecule_properties'].iloc[0]\n",
        "print(json.dumps(properties, indent=4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gTYtZXvFr6d6",
        "outputId": "1b0f98d6-e3cc-473a-81ee-2f6808f17797"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\n",
            "    \"alogp\": \"1.31\",\n",
            "    \"aromatic_rings\": 1,\n",
            "    \"cx_logd\": \"-2.16\",\n",
            "    \"cx_logp\": \"1.24\",\n",
            "    \"cx_most_apka\": \"3.41\",\n",
            "    \"cx_most_bpka\": null,\n",
            "    \"full_molformula\": \"C9H8O4\",\n",
            "    \"full_mwt\": \"180.16\",\n",
            "    \"hba\": 3,\n",
            "    \"hba_lipinski\": 4,\n",
            "    \"hbd\": 1,\n",
            "    \"hbd_lipinski\": 1,\n",
            "    \"heavy_atoms\": 13,\n",
            "    \"molecular_species\": \"ACID\",\n",
            "    \"mw_freebase\": \"180.16\",\n",
            "    \"mw_monoisotopic\": \"180.0423\",\n",
            "    \"np_likeness_score\": \"0.12\",\n",
            "    \"num_lipinski_ro5_violations\": 0,\n",
            "    \"num_ro5_violations\": 0,\n",
            "    \"psa\": \"63.60\",\n",
            "    \"qed_weighted\": \"0.55\",\n",
            "    \"ro3_pass\": \"N\",\n",
            "    \"rtb\": 2\n",
            "}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Mecanismo de acción***\n",
        "A continuación se realiza la búsqueda de moléculas interactuantes con un blanco (receptor de insulina) y se identifica su respectivo mecanismo de acción.\n",
        "*   Módulo= ***mechanism***\n",
        "*   Operador= ***only***"
      ],
      "metadata": {
        "id": "lit8yTGVjDix"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mecanismo = new_client.mechanism\n",
        "res = mecanismo.filter(target_chembl_id='CHEMBL1981' #Receptor de insulina\n",
        "                      ).only(['action_type','max_phase','mechanism_comment',\n",
        "                              'mechanism_of_action','molecule_chembl_id', 'target_chembl_id'])\n",
        "farmaco_df3= pd.DataFrame.from_records(res)\n",
        "farmaco_df3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ZBjRLhPkjCrD",
        "outputId": "2e01d801-1bcc-437a-d408-aad3025cf9f3"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   action_type  max_phase mechanism_comment         mechanism_of_action  \\\n",
              "0      AGONIST          4              None    Insulin receptor agonist   \n",
              "1      AGONIST          4              None    Insulin receptor agonist   \n",
              "2      AGONIST          4              None    Insulin receptor agonist   \n",
              "3      AGONIST          4              None    Insulin receptor agonist   \n",
              "4      AGONIST          4              None    Insulin receptor agonist   \n",
              "5      AGONIST          4              None    Insulin receptor agonist   \n",
              "6      AGONIST          4              None    Insulin receptor agonist   \n",
              "7      AGONIST          4              None    Insulin receptor agonist   \n",
              "8      AGONIST          4              None    Insulin receptor agonist   \n",
              "9      AGONIST          4              None    Insulin receptor agonist   \n",
              "10     AGONIST          4              None    Insulin receptor agonist   \n",
              "11     AGONIST          4              None    Insulin receptor agonist   \n",
              "12     AGONIST          4              None    Insulin receptor agonist   \n",
              "13     AGONIST          4              None    Insulin receptor agonist   \n",
              "14     AGONIST          4              None    Insulin receptor agonist   \n",
              "15     AGONIST          4              None    Insulin receptor agonist   \n",
              "16     AGONIST          4              None    Insulin receptor agonist   \n",
              "17     AGONIST          4              None    Insulin receptor agonist   \n",
              "18     AGONIST          4              None    Insulin receptor agonist   \n",
              "19     AGONIST          4              None    Insulin receptor agonist   \n",
              "20     AGONIST          4              None    Insulin receptor agonist   \n",
              "21     AGONIST          4              None    Insulin receptor agonist   \n",
              "22     AGONIST          4              None    Insulin receptor agonist   \n",
              "23     AGONIST          4              None    Insulin receptor agonist   \n",
              "24     AGONIST          4              None    Insulin receptor agonist   \n",
              "25     AGONIST          4              None    Insulin receptor agonist   \n",
              "26     AGONIST          4              None    Insulin receptor agonist   \n",
              "27     AGONIST          4              None    Insulin receptor agonist   \n",
              "28     AGONIST          4              None    Insulin receptor agonist   \n",
              "29     AGONIST          4              None    Insulin receptor agonist   \n",
              "30     AGONIST          4              None    Insulin receptor agonist   \n",
              "31     AGONIST          4              None    Insulin receptor agonist   \n",
              "32     AGONIST          4              None    Insulin receptor agonist   \n",
              "33     AGONIST          4              None    Insulin receptor agonist   \n",
              "34   INHIBITOR          2              None  Insulin receptor inhibitor   \n",
              "35   INHIBITOR          2              None  Insulin receptor inhibitor   \n",
              "36   INHIBITOR          3              None  Insulin receptor inhibitor   \n",
              "37     AGONIST          3              None    Insulin receptor agonist   \n",
              "38     AGONIST          4              None    Insulin receptor agonist   \n",
              "39     AGONIST          4              None    Insulin receptor agonist   \n",
              "40     AGONIST          2              None    Insulin receptor agonist   \n",
              "41     AGONIST          4              None    Insulin receptor agonist   \n",
              "42     AGONIST          4              None    Insulin receptor agonist   \n",
              "43     AGONIST          3              None    Insulin receptor agonist   \n",
              "44     AGONIST          4              None    Insulin receptor agonist   \n",
              "\n",
              "   molecule_chembl_id target_chembl_id  \n",
              "0       CHEMBL1201631       CHEMBL1981  \n",
              "1       CHEMBL1201631       CHEMBL1981  \n",
              "2       CHEMBL1201540       CHEMBL1981  \n",
              "3       CHEMBL1201629       CHEMBL1981  \n",
              "4       CHEMBL1201644       CHEMBL1981  \n",
              "5       CHEMBL1201639       CHEMBL1981  \n",
              "6       CHEMBL1201542       CHEMBL1981  \n",
              "7       CHEMBL1201543       CHEMBL1981  \n",
              "8       CHEMBL1201631       CHEMBL1981  \n",
              "9       CHEMBL1201656       CHEMBL1981  \n",
              "10      CHEMBL1201653       CHEMBL1981  \n",
              "11      CHEMBL1201635       CHEMBL1981  \n",
              "12      CHEMBL1201628       CHEMBL1981  \n",
              "13      CHEMBL1201630       CHEMBL1981  \n",
              "14      CHEMBL1201545       CHEMBL1981  \n",
              "15      CHEMBL1201638       CHEMBL1981  \n",
              "16      CHEMBL1201611       CHEMBL1981  \n",
              "17      CHEMBL1201634       CHEMBL1981  \n",
              "18      CHEMBL1201538       CHEMBL1981  \n",
              "19      CHEMBL1201613       CHEMBL1981  \n",
              "20      CHEMBL1201642       CHEMBL1981  \n",
              "21      CHEMBL1201497       CHEMBL1981  \n",
              "22      CHEMBL1201539       CHEMBL1981  \n",
              "23      CHEMBL1201640       CHEMBL1981  \n",
              "24      CHEMBL1201655       CHEMBL1981  \n",
              "25      CHEMBL1201541       CHEMBL1981  \n",
              "26      CHEMBL1201643       CHEMBL1981  \n",
              "27      CHEMBL1201627       CHEMBL1981  \n",
              "28      CHEMBL1201546       CHEMBL1981  \n",
              "29      CHEMBL1201641       CHEMBL1981  \n",
              "30      CHEMBL1201496       CHEMBL1981  \n",
              "31      CHEMBL1201664       CHEMBL1981  \n",
              "32      CHEMBL2104391       CHEMBL1981  \n",
              "33      CHEMBL2107869       CHEMBL1981  \n",
              "34       CHEMBL575448       CHEMBL1981  \n",
              "35      CHEMBL3545156       CHEMBL1981  \n",
              "36      CHEMBL1091644       CHEMBL1981  \n",
              "37      CHEMBL2108569       CHEMBL1981  \n",
              "38      CHEMBL2108493       CHEMBL1981  \n",
              "39      CHEMBL2108968       CHEMBL1981  \n",
              "40      CHEMBL2108305       CHEMBL1981  \n",
              "41      CHEMBL2108964       CHEMBL1981  \n",
              "42      CHEMBL2109046       CHEMBL1981  \n",
              "43      CHEMBL2108973       CHEMBL1981  \n",
              "44      CHEMBL2108492       CHEMBL1981  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-0ea97828-16c2-48c9-bb89-2bc7a4c77667\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>action_type</th>\n",
              "      <th>max_phase</th>\n",
              "      <th>mechanism_comment</th>\n",
              "      <th>mechanism_of_action</th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>target_chembl_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201631</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201631</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201540</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201629</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201644</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201639</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201542</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201543</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201631</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201656</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201653</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201635</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201628</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201630</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201545</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201638</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201611</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201634</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201538</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201613</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201642</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201497</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201539</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201640</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201655</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201541</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201643</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201627</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201546</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201641</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201496</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL1201664</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL2104391</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL2107869</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>INHIBITOR</td>\n",
              "      <td>2</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor inhibitor</td>\n",
              "      <td>CHEMBL575448</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>INHIBITOR</td>\n",
              "      <td>2</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor inhibitor</td>\n",
              "      <td>CHEMBL3545156</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>INHIBITOR</td>\n",
              "      <td>3</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor inhibitor</td>\n",
              "      <td>CHEMBL1091644</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>3</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL2108569</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL2108493</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL2108968</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>2</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL2108305</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL2108964</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL2109046</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>3</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL2108973</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>AGONIST</td>\n",
              "      <td>4</td>\n",
              "      <td>None</td>\n",
              "      <td>Insulin receptor agonist</td>\n",
              "      <td>CHEMBL2108492</td>\n",
              "      <td>CHEMBL1981</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0ea97828-16c2-48c9-bb89-2bc7a4c77667')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-241bc8ee-ae50-4bb0-99d6-a98eb4c349e9\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-241bc8ee-ae50-4bb0-99d6-a98eb4c349e9')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-241bc8ee-ae50-4bb0-99d6-a98eb4c349e9 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0ea97828-16c2-48c9-bb89-2bc7a4c77667 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0ea97828-16c2-48c9-bb89-2bc7a4c77667');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Alertas farmacéuticas***\n",
        "A continuación se realiza la búsqueda de compuestos que hayan presentado algún tipo de alerta farmacéutica de interés.\n",
        "*   Módulo = ***drug***\n",
        "*   Palabra clave= ***warning***\n"
      ],
      "metadata": {
        "id": "TH1eg2etZTuD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "alerta_farmaco = new_client.drug_warning\n",
        "res = alerta_farmaco.filter(warning_type='Withdrawn', warning_class='carcinogenicity') #carcinogenicity, neurotoxicity\n",
        "farmaco_df4= pd.DataFrame.from_records(res)\n",
        "farmaco_df4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "kxfOxo8HkGkB",
        "outputId": "c68a16ba-743d-4004-bb0a-42c3404754bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         efo_id efo_id_for_warning_class                     efo_term  \\\n",
              "0   EFO:0011046              EFO:0011046              carcinogenicity   \n",
              "1   EFO:0011046              EFO:0011046              carcinogenicity   \n",
              "2   EFO:0011046              EFO:0011046              carcinogenicity   \n",
              "3   EFO:0011046              EFO:0011046              carcinogenicity   \n",
              "4   EFO:0011046              EFO:0011046              carcinogenicity   \n",
              "5   EFO:0011046              EFO:0011046              carcinogenicity   \n",
              "6   EFO:0011046              EFO:0011046              carcinogenicity   \n",
              "7   EFO:0011046              EFO:0011046              carcinogenicity   \n",
              "8   EFO:0009260              EFO:0011046  non-melanoma skin carcinoma   \n",
              "9   EFO:0011046              EFO:0011046              carcinogenicity   \n",
              "10  EFO:0011046              EFO:0011046              carcinogenicity   \n",
              "11  EFO:0011046              EFO:0011046              carcinogenicity   \n",
              "12  EFO:0000707              EFO:0011046      squamous cell carcinoma   \n",
              "13  EFO:0011046              EFO:0011046              carcinogenicity   \n",
              "14  EFO:0011046              EFO:0011046              carcinogenicity   \n",
              "15  EFO:0011046              EFO:0011046              carcinogenicity   \n",
              "16  EFO:0011046              EFO:0011046              carcinogenicity   \n",
              "17  EFO:0004198              EFO:0011046                skin neoplasm   \n",
              "18  EFO:0011046              EFO:0011046              carcinogenicity   \n",
              "19  EFO:0011046              EFO:0011046              carcinogenicity   \n",
              "20  EFO:0011046              EFO:0011046              carcinogenicity   \n",
              "21  EFO:0011046              EFO:0011046              carcinogenicity   \n",
              "22  EFO:0011046              EFO:0011046              carcinogenicity   \n",
              "23  EFO:0011046              EFO:0011046              carcinogenicity   \n",
              "24  EFO:0011046              EFO:0011046              carcinogenicity   \n",
              "25  EFO:0011046              EFO:0011046              carcinogenicity   \n",
              "26  EFO:0011046              EFO:0011046              carcinogenicity   \n",
              "27  EFO:0011050              EFO:0011046    gastrointestinal toxicity   \n",
              "28  EFO:0011046              EFO:0011046              carcinogenicity   \n",
              "29  EFO:0011046              EFO:0011046              carcinogenicity   \n",
              "\n",
              "   molecule_chembl_id parent_molecule_chembl_id    warning_class  \\\n",
              "0        CHEMBL110691              CHEMBL110691  carcinogenicity   \n",
              "1        CHEMBL110691              CHEMBL110691  carcinogenicity   \n",
              "2         CHEMBL44618               CHEMBL44618  carcinogenicity   \n",
              "3       CHEMBL2104489             CHEMBL2104489  carcinogenicity   \n",
              "4        CHEMBL360328              CHEMBL360328  carcinogenicity   \n",
              "5         CHEMBL16476               CHEMBL16476  carcinogenicity   \n",
              "6       CHEMBL1788386             CHEMBL1788386  carcinogenicity   \n",
              "7       CHEMBL1788403             CHEMBL1788403  carcinogenicity   \n",
              "8       CHEMBL1863513             CHEMBL1863513  carcinogenicity   \n",
              "9         CHEMBL53418               CHEMBL53418  carcinogenicity   \n",
              "10        CHEMBL15677               CHEMBL15677  carcinogenicity   \n",
              "11      CHEMBL2105827             CHEMBL2105827  carcinogenicity   \n",
              "12      CHEMBL1863513             CHEMBL1863513  carcinogenicity   \n",
              "13          CHEMBL869                 CHEMBL869  carcinogenicity   \n",
              "14      CHEMBL1255739             CHEMBL1411979  carcinogenicity   \n",
              "15       CHEMBL462547              CHEMBL462547  carcinogenicity   \n",
              "16      CHEMBL1452696             CHEMBL1452696  carcinogenicity   \n",
              "17      CHEMBL1863513             CHEMBL1863513  carcinogenicity   \n",
              "18      CHEMBL3187246             CHEMBL1411979  carcinogenicity   \n",
              "19        CHEMBL64894              CHEMBL459265  carcinogenicity   \n",
              "20        CHEMBL16073               CHEMBL16073  carcinogenicity   \n",
              "21       CHEMBL340978              CHEMBL340978  carcinogenicity   \n",
              "22        CHEMBL46917               CHEMBL46917  carcinogenicity   \n",
              "23        CHEMBL15870               CHEMBL15870  carcinogenicity   \n",
              "24        CHEMBL63857               CHEMBL63857  carcinogenicity   \n",
              "25      CHEMBL4594242             CHEMBL4594242  carcinogenicity   \n",
              "26      CHEMBL1463345             CHEMBL1463345  carcinogenicity   \n",
              "27        CHEMBL15870               CHEMBL15870  carcinogenicity   \n",
              "28      CHEMBL1322884             CHEMBL1322884  carcinogenicity   \n",
              "29         CHEMBL1525                CHEMBL1525  carcinogenicity   \n",
              "\n",
              "                                      warning_country  \\\n",
              "0                       United States; United Kingdom   \n",
              "1                       United States; United Kingdom   \n",
              "2                                       United States   \n",
              "3                                    Germany; Austria   \n",
              "4                                       United States   \n",
              "5                                      United Kingdom   \n",
              "6                                                Oman   \n",
              "7                                                Oman   \n",
              "8                              European Union; Canada   \n",
              "9                               United States; Canada   \n",
              "10                          United Kingdom; Worldwide   \n",
              "11                                      United States   \n",
              "12                             European Union; Canada   \n",
              "13                                      United States   \n",
              "14             Germany; United States; United Kingdom   \n",
              "15  Germany; Denmark; Brazil; Egypt; Italy; Cuba; ...   \n",
              "16             Germany; Greece; United Kingdom; Egypt   \n",
              "17                             European Union; Canada   \n",
              "18             Germany; United States; United Kingdom   \n",
              "19                                           Malaysia   \n",
              "20  Malaysia; Romania; Germany; Ireland; Panama; I...   \n",
              "21      Germany; United States; United Kingdom; Spain   \n",
              "22                                               Oman   \n",
              "23          Germany; Worldwide; Spain; United Kingdom   \n",
              "24                         Oman; Canada; Saudi Arabia   \n",
              "25                                               Oman   \n",
              "26                                            Germany   \n",
              "27          Germany; Worldwide; Spain; United Kingdom   \n",
              "28                                               Oman   \n",
              "29                                               Oman   \n",
              "\n",
              "                                  warning_description  warning_id  \\\n",
              "0                       Animal carcinogenicity (dogs)        2868   \n",
              "1                       Animal carcinogenicity (dogs)        2868   \n",
              "2                       carcinogenic in mice and rats        2870   \n",
              "3                              Animal Carcinogenicity        2876   \n",
              "4                                     Carcinogenicity        2906   \n",
              "5                     Animal carcinogenicity (rodent)        2943   \n",
              "6                   Potential risk of carcinogenicity        2953   \n",
              "7                   Potential risk of carcinogenicity        3100   \n",
              "8          Increased risk of non-melanoma skin cancer        3121   \n",
              "9                   May have a carcinogenic potential        3126   \n",
              "10        Cutaneous Reactions; Animal Carcinogenicity        3219   \n",
              "11                     Increased occurrence of cancer        3224   \n",
              "12  Increased risk of skin cancer, particularly sq...        3228   \n",
              "13  mammary tumors in rats and ovarian tumors in mice        3239   \n",
              "14                   Animal carcinogenicity (rodents)        3257   \n",
              "15                                carcinogenic nature        3289   \n",
              "16  reported adverse reactions, particularly invol...        3312   \n",
              "17  Increased risk of skin cancer, particularly sq...        3318   \n",
              "18                   Animal carcinogenicity (rodents)        3347   \n",
              "19  Reports of adverse reactions and toxicity such...        3408   \n",
              "20  nephropathy; liver and kidney toxicity; carcin...        3461   \n",
              "21  Liver necrosis; Photosensitivity; Animal carci...        3510   \n",
              "22                  Potential risk of carcinogenicity        3517   \n",
              "23  serious adverse gastrointestinal reactions; tu...        3532   \n",
              "24                                   May cause cancer        3548   \n",
              "25                       Increased risk of malignancy        3640   \n",
              "26                             Animal carcinogenicity        3641   \n",
              "27  Animal Carcinogenicity (rodent); Gastrointesti...        3653   \n",
              "28                  Potential risk of carcinogenicity        3672   \n",
              "29                  Potential risk of carcinogenicity        3696   \n",
              "\n",
              "                                         warning_refs warning_type  \\\n",
              "0   [{'ref_id': '10.1177/009286150103500134', 'ref...    Withdrawn   \n",
              "1   [{'ref_id': '10.1177/009286150103500134', 'ref...    Withdrawn   \n",
              "2   [{'ref_id': '10.1021/cen-v054n016.p007', 'ref_...    Withdrawn   \n",
              "3   [{'ref_id': 'CL12.pdf', 'ref_type': 'WHO', 're...    Withdrawn   \n",
              "4   [{'ref_id': 'drug-safety-and-availability/fda-...    Withdrawn   \n",
              "5   [{'ref_id': '10.1177/009286150103500134', 'ref...    Withdrawn   \n",
              "6   [{'ref_id': 'EDM-QSM-2001.3', 'ref_type': 'WHO...    Withdrawn   \n",
              "7   [{'ref_id': 'EDM-QSM-2001.3', 'ref_type': 'WHO...    Withdrawn   \n",
              "8   [{'ref_id': 'recall-alert-rappel-avis/hc-sc/20...    Withdrawn   \n",
              "9   [{'ref_id': 'EDM-QSM-2001.3', 'ref_type': 'WHO...    Withdrawn   \n",
              "10  [{'ref_id': '7628177', 'ref_type': 'PubMed', '...    Withdrawn   \n",
              "11  [{'ref_id': 'FR-1998-10-08/html/98-26923.htm',...    Withdrawn   \n",
              "12  [{'ref_id': 'medicines/human/referrals/picato'...    Withdrawn   \n",
              "13  [{'ref_id': 'importalert_31.html', 'ref_type':...    Withdrawn   \n",
              "14  [{'ref_id': '10.1177/009286150103500134', 'ref...    Withdrawn   \n",
              "15  [{'ref_id': 'CFR-2019-title21-vol4/pdf/CFR-201...    Withdrawn   \n",
              "16  [{'ref_id': 'CL12.pdf', 'ref_type': 'WHO', 're...    Withdrawn   \n",
              "17  [{'ref_id': 'medicines/human/referrals/picato'...    Withdrawn   \n",
              "18  [{'ref_id': '10.1177/009286150103500134', 'ref...    Withdrawn   \n",
              "19  [{'ref_id': 'EDM-QSM-2001.3', 'ref_type': 'WHO...    Withdrawn   \n",
              "20  [{'ref_id': 'CL12.pdf', 'ref_type': 'WHO', 're...    Withdrawn   \n",
              "21  [{'ref_id': '10.1177/009286150103500134', 'ref...    Withdrawn   \n",
              "22  [{'ref_id': 'EDM-QSM-2001.3', 'ref_type': 'WHO...    Withdrawn   \n",
              "23  [{'ref_id': 'CL12.pdf', 'ref_type': 'WHO', 're...    Withdrawn   \n",
              "24  [{'ref_id': 'recall-alert-rappel-avis/hc-sc/20...    Withdrawn   \n",
              "25  [{'ref_id': '978-92-4-001477-0 ', 'ref_type': ...    Withdrawn   \n",
              "26  [{'ref_id': '10.1177/009286150103500134', 'ref...    Withdrawn   \n",
              "27  [{'ref_id': '10.1177/009286150103500134', 'ref...    Withdrawn   \n",
              "28  [{'ref_id': 'EDM-QSM-2001.3', 'ref_type': 'WHO...    Withdrawn   \n",
              "29  [{'ref_id': 'EDM-QSM-2001.3', 'ref_type': 'WHO...    Withdrawn   \n",
              "\n",
              "    warning_year  \n",
              "0           1970  \n",
              "1           1970  \n",
              "2           1976  \n",
              "3           1969  \n",
              "4           2020  \n",
              "5           1965  \n",
              "6           1997  \n",
              "7           1997  \n",
              "8           2020  \n",
              "9           1997  \n",
              "10          1984  \n",
              "11          1993  \n",
              "12          2020  \n",
              "13          1991  \n",
              "14          1979  \n",
              "15          1963  \n",
              "16          1984  \n",
              "17          2020  \n",
              "18          1979  \n",
              "19          1998  \n",
              "20          1965  \n",
              "21          1982  \n",
              "22          1997  \n",
              "23          1983  \n",
              "24          1997  \n",
              "25          2013  \n",
              "26          1986  \n",
              "27          1983  \n",
              "28          1997  \n",
              "29          1997  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-84f40dee-b00e-43f6-9047-ed1537617356\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>efo_id</th>\n",
              "      <th>efo_id_for_warning_class</th>\n",
              "      <th>efo_term</th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>parent_molecule_chembl_id</th>\n",
              "      <th>warning_class</th>\n",
              "      <th>warning_country</th>\n",
              "      <th>warning_description</th>\n",
              "      <th>warning_id</th>\n",
              "      <th>warning_refs</th>\n",
              "      <th>warning_type</th>\n",
              "      <th>warning_year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>CHEMBL110691</td>\n",
              "      <td>CHEMBL110691</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>United States; United Kingdom</td>\n",
              "      <td>Animal carcinogenicity (dogs)</td>\n",
              "      <td>2868</td>\n",
              "      <td>[{'ref_id': '10.1177/009286150103500134', 'ref...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>1970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>CHEMBL110691</td>\n",
              "      <td>CHEMBL110691</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>United States; United Kingdom</td>\n",
              "      <td>Animal carcinogenicity (dogs)</td>\n",
              "      <td>2868</td>\n",
              "      <td>[{'ref_id': '10.1177/009286150103500134', 'ref...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>1970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>CHEMBL44618</td>\n",
              "      <td>CHEMBL44618</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>United States</td>\n",
              "      <td>carcinogenic in mice and rats</td>\n",
              "      <td>2870</td>\n",
              "      <td>[{'ref_id': '10.1021/cen-v054n016.p007', 'ref_...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>1976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>CHEMBL2104489</td>\n",
              "      <td>CHEMBL2104489</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>Germany; Austria</td>\n",
              "      <td>Animal Carcinogenicity</td>\n",
              "      <td>2876</td>\n",
              "      <td>[{'ref_id': 'CL12.pdf', 'ref_type': 'WHO', 're...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>1969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>CHEMBL360328</td>\n",
              "      <td>CHEMBL360328</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>United States</td>\n",
              "      <td>Carcinogenicity</td>\n",
              "      <td>2906</td>\n",
              "      <td>[{'ref_id': 'drug-safety-and-availability/fda-...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>CHEMBL16476</td>\n",
              "      <td>CHEMBL16476</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>Animal carcinogenicity (rodent)</td>\n",
              "      <td>2943</td>\n",
              "      <td>[{'ref_id': '10.1177/009286150103500134', 'ref...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>1965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>CHEMBL1788386</td>\n",
              "      <td>CHEMBL1788386</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>Oman</td>\n",
              "      <td>Potential risk of carcinogenicity</td>\n",
              "      <td>2953</td>\n",
              "      <td>[{'ref_id': 'EDM-QSM-2001.3', 'ref_type': 'WHO...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>1997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>CHEMBL1788403</td>\n",
              "      <td>CHEMBL1788403</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>Oman</td>\n",
              "      <td>Potential risk of carcinogenicity</td>\n",
              "      <td>3100</td>\n",
              "      <td>[{'ref_id': 'EDM-QSM-2001.3', 'ref_type': 'WHO...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>1997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>EFO:0009260</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>non-melanoma skin carcinoma</td>\n",
              "      <td>CHEMBL1863513</td>\n",
              "      <td>CHEMBL1863513</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>European Union; Canada</td>\n",
              "      <td>Increased risk of non-melanoma skin cancer</td>\n",
              "      <td>3121</td>\n",
              "      <td>[{'ref_id': 'recall-alert-rappel-avis/hc-sc/20...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>CHEMBL53418</td>\n",
              "      <td>CHEMBL53418</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>United States; Canada</td>\n",
              "      <td>May have a carcinogenic potential</td>\n",
              "      <td>3126</td>\n",
              "      <td>[{'ref_id': 'EDM-QSM-2001.3', 'ref_type': 'WHO...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>1997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>CHEMBL15677</td>\n",
              "      <td>CHEMBL15677</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>United Kingdom; Worldwide</td>\n",
              "      <td>Cutaneous Reactions; Animal Carcinogenicity</td>\n",
              "      <td>3219</td>\n",
              "      <td>[{'ref_id': '7628177', 'ref_type': 'PubMed', '...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>1984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>CHEMBL2105827</td>\n",
              "      <td>CHEMBL2105827</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>United States</td>\n",
              "      <td>Increased occurrence of cancer</td>\n",
              "      <td>3224</td>\n",
              "      <td>[{'ref_id': 'FR-1998-10-08/html/98-26923.htm',...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>1993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>EFO:0000707</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>squamous cell carcinoma</td>\n",
              "      <td>CHEMBL1863513</td>\n",
              "      <td>CHEMBL1863513</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>European Union; Canada</td>\n",
              "      <td>Increased risk of skin cancer, particularly sq...</td>\n",
              "      <td>3228</td>\n",
              "      <td>[{'ref_id': 'medicines/human/referrals/picato'...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>CHEMBL869</td>\n",
              "      <td>CHEMBL869</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>United States</td>\n",
              "      <td>mammary tumors in rats and ovarian tumors in mice</td>\n",
              "      <td>3239</td>\n",
              "      <td>[{'ref_id': 'importalert_31.html', 'ref_type':...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>1991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>CHEMBL1255739</td>\n",
              "      <td>CHEMBL1411979</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>Germany; United States; United Kingdom</td>\n",
              "      <td>Animal carcinogenicity (rodents)</td>\n",
              "      <td>3257</td>\n",
              "      <td>[{'ref_id': '10.1177/009286150103500134', 'ref...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>1979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>CHEMBL462547</td>\n",
              "      <td>CHEMBL462547</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>Germany; Denmark; Brazil; Egypt; Italy; Cuba; ...</td>\n",
              "      <td>carcinogenic nature</td>\n",
              "      <td>3289</td>\n",
              "      <td>[{'ref_id': 'CFR-2019-title21-vol4/pdf/CFR-201...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>1963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>CHEMBL1452696</td>\n",
              "      <td>CHEMBL1452696</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>Germany; Greece; United Kingdom; Egypt</td>\n",
              "      <td>reported adverse reactions, particularly invol...</td>\n",
              "      <td>3312</td>\n",
              "      <td>[{'ref_id': 'CL12.pdf', 'ref_type': 'WHO', 're...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>1984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>EFO:0004198</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>skin neoplasm</td>\n",
              "      <td>CHEMBL1863513</td>\n",
              "      <td>CHEMBL1863513</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>European Union; Canada</td>\n",
              "      <td>Increased risk of skin cancer, particularly sq...</td>\n",
              "      <td>3318</td>\n",
              "      <td>[{'ref_id': 'medicines/human/referrals/picato'...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>CHEMBL3187246</td>\n",
              "      <td>CHEMBL1411979</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>Germany; United States; United Kingdom</td>\n",
              "      <td>Animal carcinogenicity (rodents)</td>\n",
              "      <td>3347</td>\n",
              "      <td>[{'ref_id': '10.1177/009286150103500134', 'ref...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>1979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>CHEMBL64894</td>\n",
              "      <td>CHEMBL459265</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>Malaysia</td>\n",
              "      <td>Reports of adverse reactions and toxicity such...</td>\n",
              "      <td>3408</td>\n",
              "      <td>[{'ref_id': 'EDM-QSM-2001.3', 'ref_type': 'WHO...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>1998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>CHEMBL16073</td>\n",
              "      <td>CHEMBL16073</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>Malaysia; Romania; Germany; Ireland; Panama; I...</td>\n",
              "      <td>nephropathy; liver and kidney toxicity; carcin...</td>\n",
              "      <td>3461</td>\n",
              "      <td>[{'ref_id': 'CL12.pdf', 'ref_type': 'WHO', 're...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>1965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>CHEMBL340978</td>\n",
              "      <td>CHEMBL340978</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>Germany; United States; United Kingdom; Spain</td>\n",
              "      <td>Liver necrosis; Photosensitivity; Animal carci...</td>\n",
              "      <td>3510</td>\n",
              "      <td>[{'ref_id': '10.1177/009286150103500134', 'ref...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>1982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>CHEMBL46917</td>\n",
              "      <td>CHEMBL46917</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>Oman</td>\n",
              "      <td>Potential risk of carcinogenicity</td>\n",
              "      <td>3517</td>\n",
              "      <td>[{'ref_id': 'EDM-QSM-2001.3', 'ref_type': 'WHO...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>1997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>CHEMBL15870</td>\n",
              "      <td>CHEMBL15870</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>Germany; Worldwide; Spain; United Kingdom</td>\n",
              "      <td>serious adverse gastrointestinal reactions; tu...</td>\n",
              "      <td>3532</td>\n",
              "      <td>[{'ref_id': 'CL12.pdf', 'ref_type': 'WHO', 're...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>1983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>CHEMBL63857</td>\n",
              "      <td>CHEMBL63857</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>Oman; Canada; Saudi Arabia</td>\n",
              "      <td>May cause cancer</td>\n",
              "      <td>3548</td>\n",
              "      <td>[{'ref_id': 'recall-alert-rappel-avis/hc-sc/20...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>1997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>CHEMBL4594242</td>\n",
              "      <td>CHEMBL4594242</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>Oman</td>\n",
              "      <td>Increased risk of malignancy</td>\n",
              "      <td>3640</td>\n",
              "      <td>[{'ref_id': '978-92-4-001477-0 ', 'ref_type': ...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>2013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>CHEMBL1463345</td>\n",
              "      <td>CHEMBL1463345</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>Germany</td>\n",
              "      <td>Animal carcinogenicity</td>\n",
              "      <td>3641</td>\n",
              "      <td>[{'ref_id': '10.1177/009286150103500134', 'ref...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>1986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>EFO:0011050</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>gastrointestinal toxicity</td>\n",
              "      <td>CHEMBL15870</td>\n",
              "      <td>CHEMBL15870</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>Germany; Worldwide; Spain; United Kingdom</td>\n",
              "      <td>Animal Carcinogenicity (rodent); Gastrointesti...</td>\n",
              "      <td>3653</td>\n",
              "      <td>[{'ref_id': '10.1177/009286150103500134', 'ref...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>1983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>CHEMBL1322884</td>\n",
              "      <td>CHEMBL1322884</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>Oman</td>\n",
              "      <td>Potential risk of carcinogenicity</td>\n",
              "      <td>3672</td>\n",
              "      <td>[{'ref_id': 'EDM-QSM-2001.3', 'ref_type': 'WHO...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>1997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>EFO:0011046</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>CHEMBL1525</td>\n",
              "      <td>CHEMBL1525</td>\n",
              "      <td>carcinogenicity</td>\n",
              "      <td>Oman</td>\n",
              "      <td>Potential risk of carcinogenicity</td>\n",
              "      <td>3696</td>\n",
              "      <td>[{'ref_id': 'EDM-QSM-2001.3', 'ref_type': 'WHO...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>1997</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### ***Tipo de alerta***\n",
        "Se puede realizar la búsqueda a partir de una palabra clave.\n",
        "*   Módulo= ***drug***\n",
        "*   Palabra clave= ***icontains***\n"
      ],
      "metadata": {
        "id": "7_jJYZEDlEjY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "res = alerta_farmaco.filter(warning_class__icontains='cardio')#, warning_type='Black Box Warning')\n",
        "farmaco_df5= pd.DataFrame.from_records(res)\n",
        "farmaco_df5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 904
        },
        "id": "RzxaMAkhlBew",
        "outputId": "7bf25768-f317-4c7d-f91b-79ae819be6c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          efo_id efo_id_for_warning_class        efo_term molecule_chembl_id  \\\n",
              "0           None              EFO:1001482            None       CHEMBL270190   \n",
              "1           None              EFO:1001482            None       CHEMBL270190   \n",
              "2           None              EFO:1001482            None      CHEMBL1083993   \n",
              "3           None              EFO:1001482            None          CHEMBL501   \n",
              "4           None              EFO:1001482            None      CHEMBL2362016   \n",
              "..           ...                      ...             ...                ...   \n",
              "169  EFO:1001482              EFO:1001482  cardiotoxicity        CHEMBL16080   \n",
              "170         None              EFO:1001482            None      CHEMBL4585668   \n",
              "171         None              EFO:1001482            None      CHEMBL1200322   \n",
              "172         None              EFO:1001482            None      CHEMBL2364649   \n",
              "173         None              EFO:1001482            None      CHEMBL4802239   \n",
              "\n",
              "    parent_molecule_chembl_id   warning_class warning_country  \\\n",
              "0                CHEMBL270190  cardiotoxicity   United States   \n",
              "1                CHEMBL270190  cardiotoxicity   United States   \n",
              "2                   CHEMBL633  cardiotoxicity   United States   \n",
              "3                   CHEMBL405  cardiotoxicity   United States   \n",
              "4               CHEMBL2362016  cardiotoxicity   United States   \n",
              "..                        ...             ...             ...   \n",
              "169               CHEMBL16080  cardiotoxicity   United States   \n",
              "170             CHEMBL4585668  cardiotoxicity   United States   \n",
              "171                CHEMBL1508  cardiotoxicity   United States   \n",
              "172             CHEMBL2364649  cardiotoxicity   United States   \n",
              "173             CHEMBL4650319  cardiotoxicity   United States   \n",
              "\n",
              "                                   warning_description  warning_id  \\\n",
              "0                                                 None          81   \n",
              "1                                                 None          81   \n",
              "2                                                 None          98   \n",
              "3                                                 None         109   \n",
              "4                                                 None         135   \n",
              "..                                                 ...         ...   \n",
              "169  potentially toxic to the cardiovascular system...        3710   \n",
              "170                                               None        3728   \n",
              "171                                               None        3738   \n",
              "172                                               None        3746   \n",
              "173                                               None        3784   \n",
              "\n",
              "                                          warning_refs       warning_type  \\\n",
              "0    [{'ref_id': '77a67dc6-35d3-48ff-9d18-292d4d442...  Black Box Warning   \n",
              "1    [{'ref_id': '77a67dc6-35d3-48ff-9d18-292d4d442...  Black Box Warning   \n",
              "2    [{'ref_id': '730039c2-0a32-4775-855d-98b2207e9...  Black Box Warning   \n",
              "3    [{'ref_id': 'f469fb38-0380-4621-9db3-a4f429126...  Black Box Warning   \n",
              "4    [{'ref_id': 'a482eccd-8837-47ea-904d-2f2c294d1...  Black Box Warning   \n",
              "..                                                 ...                ...   \n",
              "169  [{'ref_id': 'FR-1998-10-08/html/98-26923.htm',...          Withdrawn   \n",
              "170                                                 []  Black Box Warning   \n",
              "171                                                 []  Black Box Warning   \n",
              "172                                                 []  Black Box Warning   \n",
              "173                                                 []  Black Box Warning   \n",
              "\n",
              "     warning_year  \n",
              "0             NaN  \n",
              "1             NaN  \n",
              "2             NaN  \n",
              "3             NaN  \n",
              "4             NaN  \n",
              "..            ...  \n",
              "169        1977.0  \n",
              "170           NaN  \n",
              "171           NaN  \n",
              "172           NaN  \n",
              "173           NaN  \n",
              "\n",
              "[174 rows x 12 columns]"
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>efo_id</th>\n",
              "      <th>efo_id_for_warning_class</th>\n",
              "      <th>efo_term</th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>parent_molecule_chembl_id</th>\n",
              "      <th>warning_class</th>\n",
              "      <th>warning_country</th>\n",
              "      <th>warning_description</th>\n",
              "      <th>warning_id</th>\n",
              "      <th>warning_refs</th>\n",
              "      <th>warning_type</th>\n",
              "      <th>warning_year</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>None</td>\n",
              "      <td>EFO:1001482</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL270190</td>\n",
              "      <td>CHEMBL270190</td>\n",
              "      <td>cardiotoxicity</td>\n",
              "      <td>United States</td>\n",
              "      <td>None</td>\n",
              "      <td>81</td>\n",
              "      <td>[{'ref_id': '77a67dc6-35d3-48ff-9d18-292d4d442...</td>\n",
              "      <td>Black Box Warning</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>None</td>\n",
              "      <td>EFO:1001482</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL270190</td>\n",
              "      <td>CHEMBL270190</td>\n",
              "      <td>cardiotoxicity</td>\n",
              "      <td>United States</td>\n",
              "      <td>None</td>\n",
              "      <td>81</td>\n",
              "      <td>[{'ref_id': '77a67dc6-35d3-48ff-9d18-292d4d442...</td>\n",
              "      <td>Black Box Warning</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>None</td>\n",
              "      <td>EFO:1001482</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL1083993</td>\n",
              "      <td>CHEMBL633</td>\n",
              "      <td>cardiotoxicity</td>\n",
              "      <td>United States</td>\n",
              "      <td>None</td>\n",
              "      <td>98</td>\n",
              "      <td>[{'ref_id': '730039c2-0a32-4775-855d-98b2207e9...</td>\n",
              "      <td>Black Box Warning</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>None</td>\n",
              "      <td>EFO:1001482</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL501</td>\n",
              "      <td>CHEMBL405</td>\n",
              "      <td>cardiotoxicity</td>\n",
              "      <td>United States</td>\n",
              "      <td>None</td>\n",
              "      <td>109</td>\n",
              "      <td>[{'ref_id': 'f469fb38-0380-4621-9db3-a4f429126...</td>\n",
              "      <td>Black Box Warning</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>None</td>\n",
              "      <td>EFO:1001482</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL2362016</td>\n",
              "      <td>CHEMBL2362016</td>\n",
              "      <td>cardiotoxicity</td>\n",
              "      <td>United States</td>\n",
              "      <td>None</td>\n",
              "      <td>135</td>\n",
              "      <td>[{'ref_id': 'a482eccd-8837-47ea-904d-2f2c294d1...</td>\n",
              "      <td>Black Box Warning</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>EFO:1001482</td>\n",
              "      <td>EFO:1001482</td>\n",
              "      <td>cardiotoxicity</td>\n",
              "      <td>CHEMBL16080</td>\n",
              "      <td>CHEMBL16080</td>\n",
              "      <td>cardiotoxicity</td>\n",
              "      <td>United States</td>\n",
              "      <td>potentially toxic to the cardiovascular system...</td>\n",
              "      <td>3710</td>\n",
              "      <td>[{'ref_id': 'FR-1998-10-08/html/98-26923.htm',...</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>1977.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>None</td>\n",
              "      <td>EFO:1001482</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4585668</td>\n",
              "      <td>CHEMBL4585668</td>\n",
              "      <td>cardiotoxicity</td>\n",
              "      <td>United States</td>\n",
              "      <td>None</td>\n",
              "      <td>3728</td>\n",
              "      <td>[]</td>\n",
              "      <td>Black Box Warning</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>None</td>\n",
              "      <td>EFO:1001482</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL1200322</td>\n",
              "      <td>CHEMBL1508</td>\n",
              "      <td>cardiotoxicity</td>\n",
              "      <td>United States</td>\n",
              "      <td>None</td>\n",
              "      <td>3738</td>\n",
              "      <td>[]</td>\n",
              "      <td>Black Box Warning</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172</th>\n",
              "      <td>None</td>\n",
              "      <td>EFO:1001482</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL2364649</td>\n",
              "      <td>CHEMBL2364649</td>\n",
              "      <td>cardiotoxicity</td>\n",
              "      <td>United States</td>\n",
              "      <td>None</td>\n",
              "      <td>3746</td>\n",
              "      <td>[]</td>\n",
              "      <td>Black Box Warning</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>None</td>\n",
              "      <td>EFO:1001482</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4802239</td>\n",
              "      <td>CHEMBL4650319</td>\n",
              "      <td>cardiotoxicity</td>\n",
              "      <td>United States</td>\n",
              "      <td>None</td>\n",
              "      <td>3784</td>\n",
              "      <td>[]</td>\n",
              "      <td>Black Box Warning</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>174 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d3cd2a93-b6ef-440c-81b5-d72f7813d8c1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d3cd2a93-b6ef-440c-81b5-d72f7813d8c1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "# ***3. Blancos moleculares***\n",
        "---\n",
        "Igual que en los casos anteriores, la búsqueda de un blanco molecular se puede realizar a partir de diferentes rutas.\n",
        "Para los siguientes ejemplos consideraremos a G9a, una lisin-metiltransferasa de histonas involucrada en modificaciones epigenéticas de importancia.\n"
      ],
      "metadata": {
        "id": "_onh4YmXllK5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Búsqueda a partir del:***"
      ],
      "metadata": {
        "id": "F5EDDR-fxNuG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Nombre de un gen***\n",
        "*   Módulo = ***target***\n",
        "*   Palabra clave= ***icontains***\n",
        "*   Operador= ***only***"
      ],
      "metadata": {
        "id": "RypGVQYLluGg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "blanco_molecular = new_client.target\n",
        "gene_name = 'G9a'\n",
        "res = blanco_molecular.filter(target_synonym__icontains=gene_name).only(['organism', 'pref_name', 'target_type'])\n",
        "for i in res:\n",
        "    print(i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TcGy_IM_mUIX",
        "outputId": "c6afbf6a-5cc4-4167-b3fb-5902f9280bcb"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'organism': 'Homo sapiens', 'pref_name': 'Histone-lysine N-methyltransferase, H3 lysine-9 specific 5', 'target_type': 'SINGLE PROTEIN'}\n",
            "{'organism': 'Homo sapiens', 'pref_name': 'Histone-lysine N-methyltransferase, H3 lysine-9 specific 3', 'target_type': 'SINGLE PROTEIN'}\n",
            "{'organism': 'Mus musculus', 'pref_name': 'Histone-lysine N-methyltransferase EHMT2', 'target_type': 'SINGLE PROTEIN'}\n",
            "{'organism': 'Homo sapiens', 'pref_name': 'Histone-lysine N-methyltransferase EHMT1/EHMT2', 'target_type': 'PROTEIN FAMILY'}\n",
            "{'organism': 'Mus musculus', 'pref_name': 'Autophagy-related protein 9A', 'target_type': 'SINGLE PROTEIN'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Uniprot_id***\n",
        "El ID de Uniprot para G9a es *Q96KQ7*.\n",
        "*   Módulo = ***target***\n",
        "*   Palabra clave= ***accession***"
      ],
      "metadata": {
        "id": "-pvV9GormqnD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "uniprot_id = 'Q96KQ7'\n",
        "res = blanco_molecular.filter(target_components__accession=uniprot_id)\n",
        "blancos_df1= pd.DataFrame.from_records(res)\n",
        "blancos_df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "hY8MGaCtmvEP",
        "outputId": "cd361b70-4e0c-4aca-d95b-5dfc85e35bcc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                    cross_references      organism  \\\n",
              "0  [{'xref_id': 'Q96KQ7', 'xref_name': None, 'xre...  Homo sapiens   \n",
              "1  [{'xref_id': 'Q96KQ7', 'xref_name': None, 'xre...  Homo sapiens   \n",
              "2                                                 []  Homo sapiens   \n",
              "\n",
              "                                           pref_name  species_group_flag  \\\n",
              "0  Histone-lysine N-methyltransferase, H3 lysine-...               False   \n",
              "1  Histone-lysine N-methyltransferase, H3 lysine-...               False   \n",
              "2     Histone-lysine N-methyltransferase EHMT1/EHMT2               False   \n",
              "\n",
              "  target_chembl_id                                  target_components  \\\n",
              "0       CHEMBL6032  [{'accession': 'Q96KQ7', 'component_descriptio...   \n",
              "1       CHEMBL6032  [{'accession': 'Q96KQ7', 'component_descriptio...   \n",
              "2    CHEMBL3885593  [{'accession': 'Q9H9B1', 'component_descriptio...   \n",
              "\n",
              "      target_type  tax_id  \n",
              "0  SINGLE PROTEIN    9606  \n",
              "1  SINGLE PROTEIN    9606  \n",
              "2  PROTEIN FAMILY    9606  "
            ],
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              "      <th></th>\n",
              "      <th>cross_references</th>\n",
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              "      <th>pref_name</th>\n",
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              "      <th>0</th>\n",
              "      <td>[{'xref_id': 'Q96KQ7', 'xref_name': None, 'xre...</td>\n",
              "      <td>Homo sapiens</td>\n",
              "      <td>Histone-lysine N-methyltransferase, H3 lysine-...</td>\n",
              "      <td>False</td>\n",
              "      <td>CHEMBL6032</td>\n",
              "      <td>[{'accession': 'Q96KQ7', 'component_descriptio...</td>\n",
              "      <td>SINGLE PROTEIN</td>\n",
              "      <td>9606</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[{'xref_id': 'Q96KQ7', 'xref_name': None, 'xre...</td>\n",
              "      <td>Homo sapiens</td>\n",
              "      <td>Histone-lysine N-methyltransferase, H3 lysine-...</td>\n",
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              "          const element = document.querySelector('#df-662c2fde-2739-4135-baac-c7e84e1bdc5a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Nombre de la proteína***\n",
        "*   Módulo= ***target***\n",
        "*   Palabra clave= ***accession***\n",
        "*   Operador= ***only***"
      ],
      "metadata": {
        "id": "qMcAenf0nJLP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "protein_name = 'N-methyltransferase'\n",
        "res = blanco_molecular.filter(pref_name__icontains=protein_name).only(['organism','pref_name',\n",
        "                                                                       'target_chembl_id',\n",
        "                                                                       'target_type']).order_by(['pref_name','organism'])\n",
        "blancos_df2= pd.DataFrame.from_records(res)\n",
        "blancos_df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "6RL63NEqnIsB",
        "outputId": "6b6c948a-fb37-4cfd-db70-0c88faf28e15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           organism                                          pref_name  \\\n",
              "0      Homo sapiens  Cereblon/Histone-lysine N-methyltransferase PR...   \n",
              "1      Homo sapiens  Cereblon/Histone-lysine N-methyltransferase PR...   \n",
              "2      Homo sapiens                        Glycine N-methyltransferase   \n",
              "3      Homo sapiens               Guanidinoacetate N-methyltransferase   \n",
              "4   Cavia porcellus                      Histamine N-methyltransferase   \n",
              "..              ...                                                ...   \n",
              "61     Homo sapiens             Protein arginine N-methyltransferase 7   \n",
              "62     Homo sapiens             Protein arginine N-methyltransferase 8   \n",
              "63     Homo sapiens             Protein arginine N-methyltransferase 9   \n",
              "64     Homo sapiens  Putative histone-lysine N-methyltransferase PRDM6   \n",
              "65     Homo sapiens         VHL/Protein arginine N-methyltransferase 5   \n",
              "\n",
              "   target_chembl_id                  target_type  \n",
              "0     CHEMBL4742318  PROTEIN-PROTEIN INTERACTION  \n",
              "1     CHEMBL4742318  PROTEIN-PROTEIN INTERACTION  \n",
              "2     CHEMBL4523295               SINGLE PROTEIN  \n",
              "3     CHEMBL4523290               SINGLE PROTEIN  \n",
              "4        CHEMBL4485               SINGLE PROTEIN  \n",
              "..              ...                          ...  \n",
              "61    CHEMBL3562175               SINGLE PROTEIN  \n",
              "62    CHEMBL3108648               SINGLE PROTEIN  \n",
              "63    CHEMBL4105724               SINGLE PROTEIN  \n",
              "64    CHEMBL5214858               SINGLE PROTEIN  \n",
              "65    CHEMBL4748215  PROTEIN-PROTEIN INTERACTION  \n",
              "\n",
              "[66 rows x 4 columns]"
            ],
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              "      <th>0</th>\n",
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              "      <td>PROTEIN-PROTEIN INTERACTION</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Homo sapiens</td>\n",
              "      <td>Cereblon/Histone-lysine N-methyltransferase PR...</td>\n",
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              "      <th>2</th>\n",
              "      <td>Homo sapiens</td>\n",
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              "      <th>3</th>\n",
              "      <td>Homo sapiens</td>\n",
              "      <td>Guanidinoacetate N-methyltransferase</td>\n",
              "      <td>CHEMBL4523290</td>\n",
              "      <td>SINGLE PROTEIN</td>\n",
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              "      <th>4</th>\n",
              "      <td>Cavia porcellus</td>\n",
              "      <td>Histamine N-methyltransferase</td>\n",
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              "      <th>...</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>Homo sapiens</td>\n",
              "      <td>Protein arginine N-methyltransferase 7</td>\n",
              "      <td>CHEMBL3562175</td>\n",
              "      <td>SINGLE PROTEIN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>Homo sapiens</td>\n",
              "      <td>Protein arginine N-methyltransferase 8</td>\n",
              "      <td>CHEMBL3108648</td>\n",
              "      <td>SINGLE PROTEIN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>Homo sapiens</td>\n",
              "      <td>Protein arginine N-methyltransferase 9</td>\n",
              "      <td>CHEMBL4105724</td>\n",
              "      <td>SINGLE PROTEIN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>Homo sapiens</td>\n",
              "      <td>Putative histone-lysine N-methyltransferase PRDM6</td>\n",
              "      <td>CHEMBL5214858</td>\n",
              "      <td>SINGLE PROTEIN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>Homo sapiens</td>\n",
              "      <td>VHL/Protein arginine N-methyltransferase 5</td>\n",
              "      <td>CHEMBL4748215</td>\n",
              "      <td>PROTEIN-PROTEIN INTERACTION</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>66 rows × 4 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "# ***4. Actividades***\n",
        "---\n",
        "Con el siguiente módulo es posible obtener toda la información asociada a la actividad reportada para un compuesto o para un blanco molecular.\n",
        "A continuación se obtendrán las actividades reportadas (IC50) asociadas a un blanco.\n",
        "*   Módulo = ***activity***\n",
        "*   Palabra clave= ***_iexact***\n",
        "*   Operador= ***only***\n"
      ],
      "metadata": {
        "id": "Q-USQ9abnto0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "actividad = new_client.activity\n",
        "herg = blanco_molecular.filter(pref_name__iexact='hERG').only('target_chembl_id')[0]\n",
        "herg_activities = actividad.filter(target_chembl_id=herg['target_chembl_id']).filter(standard_type=\"IC50\")\n",
        "len(herg_activities)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J0hw7DhswolD",
        "outputId": "4fed7a8b-44ce-41c6-c930-0ef03ff256f8"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15255"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se puede obtener todas las actividasdes reportadas para un blanco especifico considerando ensayos de unión (tipo B)."
      ],
      "metadata": {
        "id": "bZqVE8xAxRu6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "res = actividad.filter(target_chembl_id='CHEMBL3938', assay_type='B') #,pchembl_value__isnull=False)\n",
        "print(len(res))\n",
        "assay_b= pd.DataFrame.from_records(res)\n",
        "assay_b"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 853
        },
        "id": "9FcN04y3xOhK",
        "outputId": "7e02326a-ae70-448e-e2dd-0af0989a4cfd"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "910\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    action_type activity_comment  activity_id  \\\n",
              "0          None       Not Active      1650747   \n",
              "1          None       Not Active      1650747   \n",
              "2          None       Not Active      1650748   \n",
              "3          None       Not Active      1650749   \n",
              "4          None             None      1650750   \n",
              "..          ...              ...          ...   \n",
              "906        None             None     24958275   \n",
              "907        None             None     24958308   \n",
              "908        None             None     24958332   \n",
              "909        None             None     24958389   \n",
              "910        None             None     24958390   \n",
              "\n",
              "                                   activity_properties assay_chembl_id  \\\n",
              "0                                                   []    CHEMBL860783   \n",
              "1                                                   []    CHEMBL860783   \n",
              "2                                                   []    CHEMBL860783   \n",
              "3                                                   []    CHEMBL860783   \n",
              "4                                                   []    CHEMBL860783   \n",
              "..                                                 ...             ...   \n",
              "906  [{'comments': None, 'relation': None, 'result_...   CHEMBL5213178   \n",
              "907  [{'comments': None, 'relation': '=', 'result_f...   CHEMBL5213310   \n",
              "908  [{'comments': None, 'relation': '=', 'result_f...   CHEMBL5213439   \n",
              "909  [{'comments': None, 'relation': '=', 'result_f...   CHEMBL5213628   \n",
              "910  [{'comments': None, 'relation': None, 'result_...   CHEMBL5213629   \n",
              "\n",
              "                                     assay_description assay_type  \\\n",
              "0    Average Binding Constant for STK16; NA=Not Act...          B   \n",
              "1    Average Binding Constant for STK16; NA=Not Act...          B   \n",
              "2    Average Binding Constant for STK16; NA=Not Act...          B   \n",
              "3    Average Binding Constant for STK16; NA=Not Act...          B   \n",
              "4    Average Binding Constant for STK16; NA=Not Act...          B   \n",
              "..                                                 ...        ...   \n",
              "906  Selectivity interaction (KinomeScan (DiscoverX...          B   \n",
              "907  Selectivity interaction (KinomeScan (DiscoverX...          B   \n",
              "908  Selectivity interaction (KinomeScan (DiscoverX...          B   \n",
              "909  Selectivity interaction (KinomeScan (DiscoverX...          B   \n",
              "910  Selectivity interaction (NanoBRET assay (HEK29...          B   \n",
              "\n",
              "    assay_variant_accession assay_variant_mutation bao_endpoint  ...  \\\n",
              "0                      None                   None  BAO_0000034  ...   \n",
              "1                      None                   None  BAO_0000034  ...   \n",
              "2                      None                   None  BAO_0000034  ...   \n",
              "3                      None                   None  BAO_0000034  ...   \n",
              "4                      None                   None  BAO_0000034  ...   \n",
              "..                      ...                    ...          ...  ...   \n",
              "906                    None                   None  BAO_0000179  ...   \n",
              "907                    None                   None  BAO_0000179  ...   \n",
              "908                    None                   None  BAO_0000179  ...   \n",
              "909                    None                   None  BAO_0000179  ...   \n",
              "910                    None                   None  BAO_0000190  ...   \n",
              "\n",
              "    target_organism                    target_pref_name target_tax_id  \\\n",
              "0      Homo sapiens  Serine/threonine-protein kinase 16          9606   \n",
              "1      Homo sapiens  Serine/threonine-protein kinase 16          9606   \n",
              "2      Homo sapiens  Serine/threonine-protein kinase 16          9606   \n",
              "3      Homo sapiens  Serine/threonine-protein kinase 16          9606   \n",
              "4      Homo sapiens  Serine/threonine-protein kinase 16          9606   \n",
              "..              ...                                 ...           ...   \n",
              "906    Homo sapiens  Serine/threonine-protein kinase 16          9606   \n",
              "907    Homo sapiens  Serine/threonine-protein kinase 16          9606   \n",
              "908    Homo sapiens  Serine/threonine-protein kinase 16          9606   \n",
              "909    Homo sapiens  Serine/threonine-protein kinase 16          9606   \n",
              "910    Homo sapiens  Serine/threonine-protein kinase 16          9606   \n",
              "\n",
              "    text_value  toid          type units    uo_units upper_value  value  \n",
              "0         None  None            Kd  None        None        None   None  \n",
              "1         None  None            Kd  None        None        None   None  \n",
              "2         None  None            Kd  None        None        None   None  \n",
              "3         None  None            Kd  None        None        None   None  \n",
              "4         None  None            Kd    uM  UO_0000065        None   0.36  \n",
              "..         ...   ...           ...   ...         ...         ...    ...  \n",
              "906       None  None  % of control     %  UO_0000187        None    3.4  \n",
              "907       None  None  % of control     %  UO_0000187        None    1.5  \n",
              "908       None  None  % of control     %  UO_0000187        None    7.7  \n",
              "909       None  None  % of control     %  UO_0000187        None   53.0  \n",
              "910       None  None          IC50    nM  UO_0000065        None  167.0  \n",
              "\n",
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              "      <td>B</td>\n",
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              "      <td>None</td>\n",
              "      <td>BAO_0000034</td>\n",
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              "      <td>Homo sapiens</td>\n",
              "      <td>Serine/threonine-protein kinase 16</td>\n",
              "      <td>9606</td>\n",
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              "      <td>None</td>\n",
              "      <td>Not Active</td>\n",
              "      <td>1650748</td>\n",
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              "      <td>CHEMBL860783</td>\n",
              "      <td>Average Binding Constant for STK16; NA=Not Act...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000034</td>\n",
              "      <td>...</td>\n",
              "      <td>Homo sapiens</td>\n",
              "      <td>Serine/threonine-protein kinase 16</td>\n",
              "      <td>9606</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>Kd</td>\n",
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              "      <td>Average Binding Constant for STK16; NA=Not Act...</td>\n",
              "      <td>B</td>\n",
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              "      <td>None</td>\n",
              "      <td>BAO_0000034</td>\n",
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              "      <td>Homo sapiens</td>\n",
              "      <td>Serine/threonine-protein kinase 16</td>\n",
              "      <td>9606</td>\n",
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              "      <td>None</td>\n",
              "      <td>Kd</td>\n",
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              "      <td>None</td>\n",
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              "      <td>None</td>\n",
              "      <td>1650750</td>\n",
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              "      <td>CHEMBL860783</td>\n",
              "      <td>Average Binding Constant for STK16; NA=Not Act...</td>\n",
              "      <td>B</td>\n",
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              "      <td>None</td>\n",
              "      <td>BAO_0000034</td>\n",
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              "      <td>Serine/threonine-protein kinase 16</td>\n",
              "      <td>9606</td>\n",
              "      <td>None</td>\n",
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              "      <td>Kd</td>\n",
              "      <td>uM</td>\n",
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              "      <td>None</td>\n",
              "      <td>0.36</td>\n",
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              "      <th>...</th>\n",
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              "      <td>24958275</td>\n",
              "      <td>[{'comments': None, 'relation': None, 'result_...</td>\n",
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              "      <td>None</td>\n",
              "      <td>BAO_0000179</td>\n",
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              "      <td>9606</td>\n",
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              "      <td>None</td>\n",
              "      <td>% of control</td>\n",
              "      <td>%</td>\n",
              "      <td>UO_0000187</td>\n",
              "      <td>None</td>\n",
              "      <td>3.4</td>\n",
              "    </tr>\n",
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              "      <th>907</th>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>24958308</td>\n",
              "      <td>[{'comments': None, 'relation': '=', 'result_f...</td>\n",
              "      <td>CHEMBL5213310</td>\n",
              "      <td>Selectivity interaction (KinomeScan (DiscoverX...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000179</td>\n",
              "      <td>...</td>\n",
              "      <td>Homo sapiens</td>\n",
              "      <td>Serine/threonine-protein kinase 16</td>\n",
              "      <td>9606</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>% of control</td>\n",
              "      <td>%</td>\n",
              "      <td>UO_0000187</td>\n",
              "      <td>None</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>908</th>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>24958332</td>\n",
              "      <td>[{'comments': None, 'relation': '=', 'result_f...</td>\n",
              "      <td>CHEMBL5213439</td>\n",
              "      <td>Selectivity interaction (KinomeScan (DiscoverX...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000179</td>\n",
              "      <td>...</td>\n",
              "      <td>Homo sapiens</td>\n",
              "      <td>Serine/threonine-protein kinase 16</td>\n",
              "      <td>9606</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>% of control</td>\n",
              "      <td>%</td>\n",
              "      <td>UO_0000187</td>\n",
              "      <td>None</td>\n",
              "      <td>7.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>909</th>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>24958389</td>\n",
              "      <td>[{'comments': None, 'relation': '=', 'result_f...</td>\n",
              "      <td>CHEMBL5213628</td>\n",
              "      <td>Selectivity interaction (KinomeScan (DiscoverX...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000179</td>\n",
              "      <td>...</td>\n",
              "      <td>Homo sapiens</td>\n",
              "      <td>Serine/threonine-protein kinase 16</td>\n",
              "      <td>9606</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>% of control</td>\n",
              "      <td>%</td>\n",
              "      <td>UO_0000187</td>\n",
              "      <td>None</td>\n",
              "      <td>53.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>910</th>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>24958390</td>\n",
              "      <td>[{'comments': None, 'relation': None, 'result_...</td>\n",
              "      <td>CHEMBL5213629</td>\n",
              "      <td>Selectivity interaction (NanoBRET assay (HEK29...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>...</td>\n",
              "      <td>Homo sapiens</td>\n",
              "      <td>Serine/threonine-protein kinase 16</td>\n",
              "      <td>9606</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>167.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>911 rows × 46 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ce0c2539-188c-4502-9928-cde188934941')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "              title=\"Suggest charts.\"\n",
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              "\n",
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              "\n",
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              "\n",
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              "\n",
              "        displayQuickchartButton(document);\n",
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              "\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ce0c2539-188c-4502-9928-cde188934941 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ce0c2539-188c-4502-9928-cde188934941');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "# ***5. Ensayos***\n",
        "---\n",
        "A continuación, se lleva a cabo una búsqueda de ensayos de inhibición relacionados con ADMET (tipo A), medidos en organismos específicos.\n",
        "\n",
        "\n",
        "*   Módulo = ***assay***\n",
        "*   Palabra clave= ***icontains***\n",
        "*   Operador= ***only***\n",
        "\n"
      ],
      "metadata": {
        "id": "KNSLCcfMoYSC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ensayo = new_client.assay\n",
        "res = ensayo.filter(description__icontains='inhibit', assay_type='A',\n",
        "                    assay_organism='Rattus norvegicus').only(['assay_chembl_id','assay_organism','description'])\n",
        "ensayos_df1= pd.DataFrame.from_records(res)\n",
        "ensayos_df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "LrTRMRDQzovI",
        "outputId": "b2f3c4b7-6455-4f80-94ec-cb138027486f"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    assay_chembl_id     assay_organism  \\\n",
              "0      CHEMBL884521  Rattus norvegicus   \n",
              "1      CHEMBL884521  Rattus norvegicus   \n",
              "2      CHEMBL615148  Rattus norvegicus   \n",
              "3      CHEMBL615199  Rattus norvegicus   \n",
              "4      CHEMBL883800  Rattus norvegicus   \n",
              "..              ...                ...   \n",
              "629   CHEMBL5157675  Rattus norvegicus   \n",
              "630   CHEMBL5160540  Rattus norvegicus   \n",
              "631   CHEMBL5160615  Rattus norvegicus   \n",
              "632   CHEMBL5160616  Rattus norvegicus   \n",
              "633   CHEMBL5160617  Rattus norvegicus   \n",
              "\n",
              "                                           description  \n",
              "0    Inhibition of cytochrome P450 progesterone 15-...  \n",
              "1    Inhibition of cytochrome P450 progesterone 15-...  \n",
              "2    Inhibition of cytochrome P450 progesterone 16-...  \n",
              "3    Inhibition of cytochrome P450 progesterone 2-a...  \n",
              "4    Inhibition of progesterone 6-beta-hydroxylase ...  \n",
              "..                                                 ...  \n",
              "629  Inhibition of phosphate uptake in CRL:CD(SD) r...  \n",
              "630  Inhibition of CYP2C19 in rat liver microsomes ...  \n",
              "631  Inhibition of CYP3A4 in rat liver microsomes a...  \n",
              "632  Inhibition of CYP2D6 in rat liver microsomes a...  \n",
              "633  Inhibition of CYP2C9 in rat liver microsomes a...  \n",
              "\n",
              "[634 rows x 3 columns]"
            ],
            "text/html": [
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              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>assay_chembl_id</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL884521</td>\n",
              "      <td>Rattus norvegicus</td>\n",
              "      <td>Inhibition of cytochrome P450 progesterone 15-...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL884521</td>\n",
              "      <td>Rattus norvegicus</td>\n",
              "      <td>Inhibition of cytochrome P450 progesterone 15-...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL615148</td>\n",
              "      <td>Rattus norvegicus</td>\n",
              "      <td>Inhibition of cytochrome P450 progesterone 16-...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL615199</td>\n",
              "      <td>Rattus norvegicus</td>\n",
              "      <td>Inhibition of cytochrome P450 progesterone 2-a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL883800</td>\n",
              "      <td>Rattus norvegicus</td>\n",
              "      <td>Inhibition of progesterone 6-beta-hydroxylase ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>629</th>\n",
              "      <td>CHEMBL5157675</td>\n",
              "      <td>Rattus norvegicus</td>\n",
              "      <td>Inhibition of phosphate uptake in CRL:CD(SD) r...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>630</th>\n",
              "      <td>CHEMBL5160540</td>\n",
              "      <td>Rattus norvegicus</td>\n",
              "      <td>Inhibition of CYP2C19 in rat liver microsomes ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>631</th>\n",
              "      <td>CHEMBL5160615</td>\n",
              "      <td>Rattus norvegicus</td>\n",
              "      <td>Inhibition of CYP3A4 in rat liver microsomes a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>632</th>\n",
              "      <td>CHEMBL5160616</td>\n",
              "      <td>Rattus norvegicus</td>\n",
              "      <td>Inhibition of CYP2D6 in rat liver microsomes a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>633</th>\n",
              "      <td>CHEMBL5160617</td>\n",
              "      <td>Rattus norvegicus</td>\n",
              "      <td>Inhibition of CYP2C9 in rat liver microsomes a...</td>\n",
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              "      async function quickchart(key) {\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-9ca67949-38b6-4bfe-a562-6520143c7fac button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ed0e5a09-989d-4da3-b7c3-6ffd2cbde23e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ed0e5a09-989d-4da3-b7c3-6ffd2cbde23e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Toxicidad***\n",
        "A continuación se muestra cómo se pueden encontrar ensayos que evalúan algún tipo de toxicidad a través del módulo *drug_warning*.\n",
        "*   Módulo = ***assay***\n",
        "*   Palabra clave= ***accession***\n",
        "*   Operador= ***only***"
      ],
      "metadata": {
        "id": "iwvbEqLI0RF4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "res = ensayo.filter(description__icontains='kidney').only(['assay_chembl_id', #Búsqueda de toxicidad en riñón.\n",
        "                                                                               'assay_organism','description'])\n",
        "ensayos_df2= pd.DataFrame.from_records(res)\n",
        "ensayos_df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Xk348l2U0dov",
        "outputId": "1422806e-fc66-4399-ecfc-777845585d01"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     assay_chembl_id       assay_organism  \\\n",
              "0       CHEMBL827088                 None   \n",
              "1       CHEMBL827088                 None   \n",
              "2       CHEMBL615234                 None   \n",
              "3       CHEMBL615699           Sus scrofa   \n",
              "4       CHEMBL615700           Sus scrofa   \n",
              "...              ...                  ...   \n",
              "7060   CHEMBL5216973         Mus musculus   \n",
              "7061   CHEMBL5217005           Sus scrofa   \n",
              "7062   CHEMBL5217006           Sus scrofa   \n",
              "7063   CHEMBL5217828  Macaca fascicularis   \n",
              "7064   CHEMBL5217902         Mus musculus   \n",
              "\n",
              "                                            description  \n",
              "0     Percent inhibition against 11-beta-hydroxyster...  \n",
              "1     Percent inhibition against 11-beta-hydroxyster...  \n",
              "2     Percent inhibition against 11-beta-hydroxyster...  \n",
              "3     Percent inhibition was measured against 5,10-M...  \n",
              "4     Percent inhibition was measured against 5,10-M...  \n",
              "...                                                 ...  \n",
              "7060  Toxicity in nude BALB/c nude mouse xenografted...  \n",
              "7061  Inhibition of porcine kidney DAAO using D-seri...  \n",
              "7062  Inhibition of porcine kidney DAAO using D-seri...  \n",
              "7063  Renal toxicity in cynomolgus monkey assessed a...  \n",
              "7064  Toxicity in mouse infected with Candida albica...  \n",
              "\n",
              "[7065 rows x 3 columns]"
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              "      <th></th>\n",
              "      <th>assay_chembl_id</th>\n",
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              "      <th>0</th>\n",
              "      <td>CHEMBL827088</td>\n",
              "      <td>None</td>\n",
              "      <td>Percent inhibition against 11-beta-hydroxyster...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL827088</td>\n",
              "      <td>None</td>\n",
              "      <td>Percent inhibition against 11-beta-hydroxyster...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL615234</td>\n",
              "      <td>None</td>\n",
              "      <td>Percent inhibition against 11-beta-hydroxyster...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL615699</td>\n",
              "      <td>Sus scrofa</td>\n",
              "      <td>Percent inhibition was measured against 5,10-M...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL615700</td>\n",
              "      <td>Sus scrofa</td>\n",
              "      <td>Percent inhibition was measured against 5,10-M...</td>\n",
              "    </tr>\n",
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              "      <th>7060</th>\n",
              "      <td>CHEMBL5216973</td>\n",
              "      <td>Mus musculus</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>7061</th>\n",
              "      <td>CHEMBL5217005</td>\n",
              "      <td>Sus scrofa</td>\n",
              "      <td>Inhibition of porcine kidney DAAO using D-seri...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7062</th>\n",
              "      <td>CHEMBL5217006</td>\n",
              "      <td>Sus scrofa</td>\n",
              "      <td>Inhibition of porcine kidney DAAO using D-seri...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7063</th>\n",
              "      <td>CHEMBL5217828</td>\n",
              "      <td>Macaca fascicularis</td>\n",
              "      <td>Renal toxicity in cynomolgus monkey assessed a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7064</th>\n",
              "      <td>CHEMBL5217902</td>\n",
              "      <td>Mus musculus</td>\n",
              "      <td>Toxicity in mouse infected with Candida albica...</td>\n",
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              "  </tbody>\n",
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              "<p>7065 rows × 3 columns</p>\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-b1e2c3e0-82d9-4321-818e-c7d948742424 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1d1dfa0a-f7c6-48a2-9bc2-74778aa4bcb1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1d1dfa0a-f7c6-48a2-9bc2-74778aa4bcb1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "# ***6. Tejido***\n",
        "---\n"
      ],
      "metadata": {
        "id": "XJjthkPn1QpV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Búsqueda a partir de:***"
      ],
      "metadata": {
        "id": "QyuSHPgp3Imm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Uberon id***\n",
        "Puedes obtener un tejido en particular usado ***Uberon id*** (UBERON es una ontologia de anatomía entre especies).\n",
        "\n",
        "Para más información visita la siguiente [liga](https://www.ebi.ac.uk/ols/ontologies/uberon#:~:text=Uberon%20is%20an%20integrated%20cross,structure%2C%20function%20and%20developmental%20lineage).\n",
        "\n",
        "*   Módulo = ***tissue***\n"
      ],
      "metadata": {
        "id": "F4d0e8vH3Dbq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tejido = new_client.tissue\n",
        "res = tejido.filter(uberon_id=\"UBERON:0000177\")#Pus\n",
        "ensayos_df3= pd.DataFrame.from_records(res)\n",
        "ensayos_df3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "xAQMH4mQ1q8s",
        "outputId": "4d6a0735-c538-4e96-c361-7fbb9f1026b5"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  bto_id caloha_id efo_id pref_name tissue_chembl_id       uberon_id\n",
              "0   None      None   None       Pus    CHEMBL3987929  UBERON:0000177\n",
              "1   None      None   None       Pus    CHEMBL3987929  UBERON:0000177"
            ],
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-952202c0-beaf-42fd-a22b-02345492eaa2\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-952202c0-beaf-42fd-a22b-02345492eaa2')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-952202c0-beaf-42fd-a22b-02345492eaa2 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6949b831-a9cb-4aec-a85d-08315a2a5025 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6949b831-a9cb-4aec-a85d-08315a2a5025');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Nombre del tejido***\n",
        "A continuaciónse obtiene la información de los tejidos que contienen la palabra \"blood\" en su nombre.\n",
        "*   Palabra clave= ***istarthwith***\n"
      ],
      "metadata": {
        "id": "AL-WnJs915Xb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "res = tejido.filter(pref_name__icontains='bra') #Cerebro\n",
        "ensayos_df4= pd.DataFrame.from_records(res)\n",
        "ensayos_df4[0:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "-TpFosa1177z",
        "outputId": "f10e0fb1-a923-497f-fc52-fe0f37879883"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        bto_id caloha_id       efo_id            pref_name tissue_chembl_id  \\\n",
              "0         None      None         None  Blood brain barrier    CHEMBL3987461   \n",
              "1         None      None         None  Blood brain barrier    CHEMBL3987461   \n",
              "2  BTO:0000142   TS-0095  EFO:0000302                Brain    CHEMBL3638188   \n",
              "\n",
              "        uberon_id  \n",
              "0  UBERON:0000120  \n",
              "1  UBERON:0000120  \n",
              "2  UBERON:0000955  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-9282d963-c208-4b3f-9b06-76d0573e10b5\">\n",
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              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>bto_id</th>\n",
              "      <th>caloha_id</th>\n",
              "      <th>efo_id</th>\n",
              "      <th>pref_name</th>\n",
              "      <th>tissue_chembl_id</th>\n",
              "      <th>uberon_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>Blood brain barrier</td>\n",
              "      <td>CHEMBL3987461</td>\n",
              "      <td>UBERON:0000120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>Blood brain barrier</td>\n",
              "      <td>CHEMBL3987461</td>\n",
              "      <td>UBERON:0000120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BTO:0000142</td>\n",
              "      <td>TS-0095</td>\n",
              "      <td>EFO:0000302</td>\n",
              "      <td>Brain</td>\n",
              "      <td>CHEMBL3638188</td>\n",
              "      <td>UBERON:0000955</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9282d963-c208-4b3f-9b06-76d0573e10b5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-fb5c5b3a-93e4-46ce-86e4-a42f675766b0\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fb5c5b3a-93e4-46ce-86e4-a42f675766b0')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-fb5c5b3a-93e4-46ce-86e4-a42f675766b0 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9282d963-c208-4b3f-9b06-76d0573e10b5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9282d963-c208-4b3f-9b06-76d0573e10b5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***BTO ID***\n",
        "Obtener tejido por ***BTO ID*** (Ontologia de Tejidos Brenda).\n",
        "\n",
        "Para más información visita la siguiente [liga](https://www.ebi.ac.uk/ols/ontologies/bto#:~:text=A%20structured%20controlled%20vocabulary%20for,cell%20types%20and%20cell%20cultures).\n"
      ],
      "metadata": {
        "id": "yEH_nMlV2KKT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "res = tejido.filter(bto_id=\"BTO:0001307\") #Estómago\n",
        "ensayos_df5= pd.DataFrame.from_records(res)\n",
        "ensayos_df5[0:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "l7Rh21wV2gF4",
        "outputId": "b4e2c8b8-846a-436d-98ba-a191249aec05"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        bto_id caloha_id       efo_id pref_name tissue_chembl_id  \\\n",
              "0  BTO:0001307   TS-0980  EFO:0000837   Stomach    CHEMBL3638185   \n",
              "1  BTO:0001307   TS-0980  EFO:0000837   Stomach    CHEMBL3638185   \n",
              "\n",
              "        uberon_id  \n",
              "0  UBERON:0000945  \n",
              "1  UBERON:0000945  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-8eebd8e3-0a97-4b47-8f06-034272e63845\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>bto_id</th>\n",
              "      <th>caloha_id</th>\n",
              "      <th>efo_id</th>\n",
              "      <th>pref_name</th>\n",
              "      <th>tissue_chembl_id</th>\n",
              "      <th>uberon_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BTO:0001307</td>\n",
              "      <td>TS-0980</td>\n",
              "      <td>EFO:0000837</td>\n",
              "      <td>Stomach</td>\n",
              "      <td>CHEMBL3638185</td>\n",
              "      <td>UBERON:0000945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BTO:0001307</td>\n",
              "      <td>TS-0980</td>\n",
              "      <td>EFO:0000837</td>\n",
              "      <td>Stomach</td>\n",
              "      <td>CHEMBL3638185</td>\n",
              "      <td>UBERON:0000945</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8eebd8e3-0a97-4b47-8f06-034272e63845')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-d222a93e-2b1a-4b6b-bd55-8407ac0630e9\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d222a93e-2b1a-4b6b-bd55-8407ac0630e9')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-d222a93e-2b1a-4b6b-bd55-8407ac0630e9 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8eebd8e3-0a97-4b47-8f06-034272e63845 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8eebd8e3-0a97-4b47-8f06-034272e63845');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***EFO ID***\n",
        "Obtener tejidos por ***EFO id*** (Ontologia de Factores Experimentales).\n",
        "\n",
        "Para más información visita la siguiente [liga](https://www.ebi.ac.uk/efo/).\n",
        "\n"
      ],
      "metadata": {
        "id": "pMDJiKnd2seS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "res = tejido.filter(efo_id=\"EFO:0000967\") #Cuello\n",
        "ensayos_df6= pd.DataFrame.from_records(res)\n",
        "ensayos_df6[0:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "Q99b1kqS3C0e",
        "outputId": "8850c1af-545e-4ba4-942e-95a7cafd65c5"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        bto_id caloha_id       efo_id pref_name tissue_chembl_id  \\\n",
              "0  BTO:0000420   TS-2045  EFO:0000967      Neck    CHEMBL3987857   \n",
              "1  BTO:0000420   TS-2045  EFO:0000967      Neck    CHEMBL3987857   \n",
              "\n",
              "        uberon_id  \n",
              "0  UBERON:0000974  \n",
              "1  UBERON:0000974  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-428974a7-ae49-4994-84b9-921df5861937\">\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>bto_id</th>\n",
              "      <th>caloha_id</th>\n",
              "      <th>efo_id</th>\n",
              "      <th>pref_name</th>\n",
              "      <th>tissue_chembl_id</th>\n",
              "      <th>uberon_id</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BTO:0000420</td>\n",
              "      <td>TS-2045</td>\n",
              "      <td>EFO:0000967</td>\n",
              "      <td>Neck</td>\n",
              "      <td>CHEMBL3987857</td>\n",
              "      <td>UBERON:0000974</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BTO:0000420</td>\n",
              "      <td>TS-2045</td>\n",
              "      <td>EFO:0000967</td>\n",
              "      <td>Neck</td>\n",
              "      <td>CHEMBL3987857</td>\n",
              "      <td>UBERON:0000974</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-428974a7-ae49-4994-84b9-921df5861937')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-4ae3afba-9bfd-4bd9-ab01-99bc7be9b6df\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4ae3afba-9bfd-4bd9-ab01-99bc7be9b6df')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-4ae3afba-9bfd-4bd9-ab01-99bc7be9b6df button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-428974a7-ae49-4994-84b9-921df5861937 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-428974a7-ae49-4994-84b9-921df5861937');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "# ***7. Células***\n",
        "---"
      ],
      "metadata": {
        "id": "dkYCIlg93Pon"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Búsqueda a partir de:***"
      ],
      "metadata": {
        "id": "Pmf6lqNG6KAW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Cellosaurus id***\n",
        "Para mayor información visita la siguiente [liga](https://www.cellosaurus.org/)\n",
        "*   Módulo= ***cell_line***\n"
      ],
      "metadata": {
        "id": "2Bb0RNDP6Hj-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "linea_celular = new_client.cell_line\n",
        "res = linea_celular.filter(cellosaurus_id=\"CVCL_0417\") #Breast metastasis of melanoma cells\n",
        "ensayos_df7= pd.DataFrame.from_records(res)\n",
        "ensayos_df7[0:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        },
        "id": "UUFfdX1M3ZxM",
        "outputId": "a356730e-a312-4aa4-8b5a-c436d40cb995"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  cell_chembl_id                                 cell_description  cell_id  \\\n",
              "0  CHEMBL3307686  MDA-MB-435 (Breast metastasis of melanoma cells      687   \n",
              "1  CHEMBL3307686  MDA-MB-435 (Breast metastasis of melanoma cells      687   \n",
              "\n",
              "    cell_name cell_source_organism  cell_source_tax_id  \\\n",
              "0  MDA-MB-435         Homo sapiens                9606   \n",
              "1  MDA-MB-435         Homo sapiens                9606   \n",
              "\n",
              "                    cell_source_tissue cellosaurus_id cl_lincs_id clo_id  \\\n",
              "0  Breast metastasis of melanoma cells      CVCL_0417        None   None   \n",
              "1  Breast metastasis of melanoma cells      CVCL_0417        None   None   \n",
              "\n",
              "        efo_id  \n",
              "0  EFO_0001213  \n",
              "1  EFO_0001213  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f2eac3ba-5582-4ca0-87b8-ec6296ca1e59\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cell_chembl_id</th>\n",
              "      <th>cell_description</th>\n",
              "      <th>cell_id</th>\n",
              "      <th>cell_name</th>\n",
              "      <th>cell_source_organism</th>\n",
              "      <th>cell_source_tax_id</th>\n",
              "      <th>cell_source_tissue</th>\n",
              "      <th>cellosaurus_id</th>\n",
              "      <th>cl_lincs_id</th>\n",
              "      <th>clo_id</th>\n",
              "      <th>efo_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL3307686</td>\n",
              "      <td>MDA-MB-435 (Breast metastasis of melanoma cells</td>\n",
              "      <td>687</td>\n",
              "      <td>MDA-MB-435</td>\n",
              "      <td>Homo sapiens</td>\n",
              "      <td>9606</td>\n",
              "      <td>Breast metastasis of melanoma cells</td>\n",
              "      <td>CVCL_0417</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>EFO_0001213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL3307686</td>\n",
              "      <td>MDA-MB-435 (Breast metastasis of melanoma cells</td>\n",
              "      <td>687</td>\n",
              "      <td>MDA-MB-435</td>\n",
              "      <td>Homo sapiens</td>\n",
              "      <td>9606</td>\n",
              "      <td>Breast metastasis of melanoma cells</td>\n",
              "      <td>CVCL_0417</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>EFO_0001213</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f2eac3ba-5582-4ca0-87b8-ec6296ca1e59')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f2eac3ba-5582-4ca0-87b8-ec6296ca1e59 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f2eac3ba-5582-4ca0-87b8-ec6296ca1e59');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Descripción***\n",
        "En este caso se obtendrán células descritas como linfoma.\n",
        "*   Palabra clave= ***icontains***"
      ],
      "metadata": {
        "id": "C4jljaZA3qOM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "res = linea_celular.filter(cell_description__icontains=\"lymphoma\")\n",
        "ensayos_df8= pd.DataFrame.from_records(res)\n",
        "ensayos_df8[0:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "id": "cXoKQNPy3vRL",
        "outputId": "f875ae53-defe-4560-8e76-780d0b653960"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  cell_chembl_id                    cell_description  cell_id cell_name  \\\n",
              "0  CHEMBL3308488      CA46 (Burkitts lymphoma cells)      346      CA46   \n",
              "1  CHEMBL3308488      CA46 (Burkitts lymphoma cells)      346      CA46   \n",
              "2  CHEMBL3308006  U-937 (Histiocytic lymphoma cells)      379     U-937   \n",
              "\n",
              "  cell_source_organism  cell_source_tax_id          cell_source_tissue  \\\n",
              "0         Homo sapiens              9606.0     Burkitts lymphoma cells   \n",
              "1         Homo sapiens              9606.0     Burkitts lymphoma cells   \n",
              "2         Homo sapiens              9606.0  Histiocytic lymphoma cells   \n",
              "\n",
              "  cellosaurus_id cl_lincs_id       clo_id       efo_id  \n",
              "0      CVCL_1101    LCL-2017  CLO_0002168  EFO_0002124  \n",
              "1      CVCL_1101    LCL-2017  CLO_0002168  EFO_0002124  \n",
              "2      CVCL_0007    LCL-1125  CLO_0009465  EFO_0001257  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dbc432f5-08de-4b4e-a0d1-86bdf7aef845\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cell_chembl_id</th>\n",
              "      <th>cell_description</th>\n",
              "      <th>cell_id</th>\n",
              "      <th>cell_name</th>\n",
              "      <th>cell_source_organism</th>\n",
              "      <th>cell_source_tax_id</th>\n",
              "      <th>cell_source_tissue</th>\n",
              "      <th>cellosaurus_id</th>\n",
              "      <th>cl_lincs_id</th>\n",
              "      <th>clo_id</th>\n",
              "      <th>efo_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL3308488</td>\n",
              "      <td>CA46 (Burkitts lymphoma cells)</td>\n",
              "      <td>346</td>\n",
              "      <td>CA46</td>\n",
              "      <td>Homo sapiens</td>\n",
              "      <td>9606.0</td>\n",
              "      <td>Burkitts lymphoma cells</td>\n",
              "      <td>CVCL_1101</td>\n",
              "      <td>LCL-2017</td>\n",
              "      <td>CLO_0002168</td>\n",
              "      <td>EFO_0002124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL3308488</td>\n",
              "      <td>CA46 (Burkitts lymphoma cells)</td>\n",
              "      <td>346</td>\n",
              "      <td>CA46</td>\n",
              "      <td>Homo sapiens</td>\n",
              "      <td>9606.0</td>\n",
              "      <td>Burkitts lymphoma cells</td>\n",
              "      <td>CVCL_1101</td>\n",
              "      <td>LCL-2017</td>\n",
              "      <td>CLO_0002168</td>\n",
              "      <td>EFO_0002124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL3308006</td>\n",
              "      <td>U-937 (Histiocytic lymphoma cells)</td>\n",
              "      <td>379</td>\n",
              "      <td>U-937</td>\n",
              "      <td>Homo sapiens</td>\n",
              "      <td>9606.0</td>\n",
              "      <td>Histiocytic lymphoma cells</td>\n",
              "      <td>CVCL_0007</td>\n",
              "      <td>LCL-1125</td>\n",
              "      <td>CLO_0009465</td>\n",
              "      <td>EFO_0001257</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dbc432f5-08de-4b4e-a0d1-86bdf7aef845')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dbc432f5-08de-4b4e-a0d1-86bdf7aef845 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dbc432f5-08de-4b4e-a0d1-86bdf7aef845');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "# ***8. Utilidades***\n",
        "---\n"
      ],
      "metadata": {
        "id": "JhOYrfGG37jN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Convertir SMILES a CTAB***\n",
        "*   Módulo = ***utils***\n",
        "\n",
        "\n",
        "CTAB: es un archivo tipo mol o sdf. \"Connection Table\". CTAB es una representación de texto plano utilizada para describir la estructura molecular de una sustancia química.\n",
        "\n"
      ],
      "metadata": {
        "id": "NfaPukH04eId"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "aspirina = utils.smiles2ctab('O=C(Oc1ccccc1C(=O)O)C')\n",
        "aspirina"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "PeDem1bR4rkQ",
        "outputId": "f98f76b9-7531-4d22-b058-2aef0bb42f6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\n     RDKit          2D\\n\\n 13 13  0  0  0  0  0  0  0  0999 V2000\\n   -1.8012   -1.1888    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.7990   -0.1888    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.9320    0.3096    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.0672   -0.1922    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.0690   -1.1922    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.7960   -1.6940    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.6630   -1.1956    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.6650   -0.1956    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.8000    0.3062    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.8020    1.3060    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.0630    1.8078    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.6690    1.8042    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.6640    0.3130    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n  1  2  2  0\\n  2  3  1  0\\n  3  4  1  0\\n  4  5  2  0\\n  5  6  1  0\\n  6  7  2  0\\n  7  8  1  0\\n  8  9  2  0\\n  9 10  1  0\\n 10 11  2  0\\n 10 12  1  0\\n  2 13  1  0\\n  9  4  1  0\\nM  END\\n$$$$\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Calcular...***"
      ],
      "metadata": {
        "id": "hDEuR5GU8EwT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***La subestructura común máxima***\n",
        "Permite encontrar la porción más significativa y compartida de las estructuras moleculares,"
      ],
      "metadata": {
        "id": "k7Rzh4ku5qdm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "smiles = [\"O=C(NCc1cc(OC)c(O)cc1)CCCC/C=C/C(C)C\",\n",
        "          \"CC(C)CCCCCC(=O)NCC1=CC(=C(C=C1)O)OC\", \"c1(C=O)cc(OC)c(O)cc1\"]\n",
        "mols = [utils.smiles2ctab(smile) for smile in smiles]\n",
        "sdf = ''.join(mols)\n",
        "result = utils.mcs(sdf)\n",
        "result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "qj745WxZ5_5r",
        "outputId": "383c7080-579f-48d7-8b12-166db5245ab2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'[#6]1(-[#6]):[#6]:[#6](-[#8]-[#6]):[#6](:[#6]:[#6]:1)-[#8]'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Descriptores moleculares***"
      ],
      "metadata": {
        "id": "N1V0CUJQ6ErR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "aspirina = utils.smiles2ctab('O=C(Oc1ccccc1C(=O)O)C')\n",
        "descs = json.loads(utils.chemblDescriptors(aspirina))[0]\n",
        "descs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ehrsEVlI6L2i",
        "outputId": "ea48d715-a68b-4635-cef9-01ea0bfc398d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'qed': 0.5501217966938848,\n",
              " 'MolWt': 180.15899999999996,\n",
              " 'TPSA': 63.60000000000001,\n",
              " 'HeavyAtomCount': 13,\n",
              " 'NumAromaticRings': 1,\n",
              " 'NumHAcceptors': 3,\n",
              " 'NumHDonors': 1,\n",
              " 'NumRotatableBonds': 2,\n",
              " 'MolLogP': 1.3100999999999998,\n",
              " 'MolecularFormula': 'C9H8O4',\n",
              " 'Ro3Pass': 0,\n",
              " 'NumRo5': 0,\n",
              " 'MonoisotopicMolWt': 180.042258736}"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Alertas estructurales***"
      ],
      "metadata": {
        "id": "SQRuYo626v6c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "alerts = json.loads(utils.structuralAlerts(aspirina))\n",
        "for a in alerts[0]:\n",
        "    print(a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5WFH2ay163_d",
        "outputId": "a66241fc-2474-4e61-8464-671807f37e88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'alert_id': 1030, 'alert_name': 'Ester', 'set_name': 'MLSMR', 'smarts': '[#6]-C(=O)O-[#6]'}\n",
            "{'alert_id': 1069, 'alert_name': 'vinyl michael acceptor1', 'set_name': 'MLSMR', 'smarts': '[#6]-[CH1]=C-C(=O)[#6,#7,#8]'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Molécula madre a partir de su identificador SMILES***\n"
      ],
      "metadata": {
        "id": "HUTbCoNn7YpJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "par = json.loads(utils.getParent(mol))\n",
        "par"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sqZW9y5P7qJC",
        "outputId": "7abd8c5a-9f00-4e84-9c2c-a7db096a2e3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'parent_molblock': '\\n     RDKit          2D\\n\\n 18 18  0  0  0  0  0  0  0  0999 V2000\\n   -4.9566   -0.4188    0.0000 Na  0  0  0  0  0  1  0  0  0  0  0  0\\n   -4.0888   -0.9158    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.2246   -0.4126    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.2282    0.5874    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.3568   -0.9096    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.4926   -0.4064    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.6248   -0.9034    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.2394   -0.4002    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.2360    0.5998    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.1000    1.1028    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.9680    0.6060    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.6320    1.0968    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.4960    0.5936    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    4.7721    0.9028    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    4.5695   -0.0764    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\\n    3.5755   -0.1862    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\\n    3.1639    0.7252    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\\n    3.9035    1.3982    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\\n  1  2  1  0\\n  2  3  1  0\\n  3  4  2  0\\n  3  5  1  0\\n  5  6  1  0\\n  6  7  2  0\\n  7  8  1  0\\n  8  9  2  0\\n  9 10  1  0\\n 10 11  1  0\\n  9 12  1  0\\n 12 13  2  0\\n 13  6  1  0\\n 14 15  2  0\\n 15 16  1  0\\n 16 17  2  0\\n 17 18  1  0\\n 18 14  1  0\\nM  END\\n',\n",
              "  'exclude': False}]"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Estandarizar una molécula***"
      ],
      "metadata": {
        "id": "RG3-r8Oa7Ih0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mol = utils.smiles2ctab(\"[Na]OC(=O)Cc1ccc(C[NH3+])cc1.c1nnn[n-]1.O\")\n",
        "st = json.loads(utils.standardize(mol))\n",
        "st"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b6d6wUIT7MP1",
        "outputId": "5d95b07a-d364-4250-d8d3-946fcf4f217e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'standard_molblock': '\\n     RDKit          2D\\n\\n 19 17  0  0  0  0  0  0  0  0999 V2000\\n   -1.4275   -2.9831    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.9566   -0.4188    0.0000 Na  0  0  0  0  0 15  0  0  0  0  0  0\\n   -4.0888   -0.9158    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.2246   -0.4126    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.2282    0.5874    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.3568   -0.9096    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.4926   -0.4064    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.6248   -0.9034    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.2394   -0.4002    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.2360    0.5998    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.1000    1.1028    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.9680    0.6060    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.6320    1.0968    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.4960    0.5936    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    4.7721    0.9028    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    4.5695   -0.0764    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\\n    3.5755   -0.1862    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\\n    3.1639    0.7252    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\\n    3.9035    1.3982    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\\n  3  4  1  0\\n  4  5  2  0\\n  4  6  1  0\\n  6  7  1  0\\n  7  8  2  0\\n  8  9  1  0\\n  9 10  2  0\\n 10 11  1  0\\n 11 12  1  0\\n 10 13  1  0\\n 13 14  2  0\\n 14  7  1  0\\n 15 16  2  0\\n 16 17  1  0\\n 17 18  2  0\\n 18 19  1  0\\n 19 15  1  0\\nM  CHG  2   2   1   3  -1\\nM  END\\n'}]"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "# ***9. Ejercicio global***\n",
        "---\n"
      ],
      "metadata": {
        "id": "xuwhchwXp1WN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A continuación se desarrollará un ejercicio global partiendo de la búsqueda de moléculas pequeñas en los fármacos aprobados (fase clínica 4) y en vía de aprobación (fase clínica 3), seguido del desglose de sus propiedades para finalizar con el curado (aplicación de filtros) al dataframe obtenido."
      ],
      "metadata": {
        "id": "pYOSWjQH-Jv8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "farmacos_aprobados = molecula.filter(max_phase=4\n",
        "                                #atc_classifications__level2='L04', #Clasificación ATC de inmunosupresores\n",
        "                                ).only(['oral','parenteral','topical', 'molecule_properties'#'black_box_warning',\n",
        "                                        'canonical_smiles','first_approval','indication_class', 'molecule_structures'\n",
        "                                        'max_phase','pref_name','molecule_type', 'molecule_chembl_id'])\n",
        "farmaco= pd.DataFrame.from_records(farmacos_aprobados)\n",
        "print(farmaco.info())\n",
        "farmaco['canonical_smiles'] = farmaco.loc[ farmaco['molecule_structures'].notnull(), 'molecule_structures'].apply(lambda x: x['canonical_smiles']) #This is the mwt of the parent compound\n",
        "farmaco=farmaco.drop_duplicates(subset='molecule_chembl_id', keep='first')\n",
        "print(farmaco.info())\n",
        "farmaco"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "88dNFFm68Udx",
        "outputId": "a7a61763-ae3c-451b-d1ea-508a15af5785"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4193 entries, 0 to 4192\n",
            "Data columns (total 11 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   first_approval       3216 non-null   float64\n",
            " 1   indication_class     2241 non-null   object \n",
            " 2   max_phase            4193 non-null   object \n",
            " 3   molecule_chembl_id   4193 non-null   object \n",
            " 4   molecule_properties  3641 non-null   object \n",
            " 5   molecule_structures  3493 non-null   object \n",
            " 6   molecule_type        4193 non-null   object \n",
            " 7   oral                 4193 non-null   bool   \n",
            " 8   parenteral           4193 non-null   bool   \n",
            " 9   pref_name            4193 non-null   object \n",
            " 10  topical              4193 non-null   bool   \n",
            "dtypes: bool(3), float64(1), object(7)\n",
            "memory usage: 274.5+ KB\n",
            "None\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 4192 entries, 0 to 4192\n",
            "Data columns (total 12 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   first_approval       3215 non-null   float64\n",
            " 1   indication_class     2240 non-null   object \n",
            " 2   max_phase            4192 non-null   object \n",
            " 3   molecule_chembl_id   4192 non-null   object \n",
            " 4   molecule_properties  3640 non-null   object \n",
            " 5   molecule_structures  3492 non-null   object \n",
            " 6   molecule_type        4192 non-null   object \n",
            " 7   oral                 4192 non-null   bool   \n",
            " 8   parenteral           4192 non-null   bool   \n",
            " 9   pref_name            4192 non-null   object \n",
            " 10  topical              4192 non-null   bool   \n",
            " 11  canonical_smiles     3492 non-null   object \n",
            "dtypes: bool(3), float64(1), object(8)\n",
            "memory usage: 339.8+ KB\n",
            "None\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      first_approval           indication_class max_phase molecule_chembl_id  \\\n",
              "0             1976.0           Antihypertensive       4.0            CHEMBL2   \n",
              "2             1984.0  Smoking Cessation Adjunct       4.0            CHEMBL3   \n",
              "3             1990.0              Antibacterial       4.0            CHEMBL4   \n",
              "4             1964.0              Antibacterial       4.0            CHEMBL5   \n",
              "5             1965.0          Anti-Inflammatory       4.0            CHEMBL6   \n",
              "...              ...                        ...       ...                ...   \n",
              "4188          2022.0                       None       4.0      CHEMBL5095051   \n",
              "4189          2021.0                       None       4.0      CHEMBL5095052   \n",
              "4190          2022.0                       None       4.0      CHEMBL5095420   \n",
              "4191          2020.0                       None       4.0      CHEMBL5095496   \n",
              "4192          2015.0                       None       4.0      CHEMBL5095505   \n",
              "\n",
              "                                    molecule_properties  \\\n",
              "0     {'alogp': '1.78', 'aromatic_rings': 3, 'cx_log...   \n",
              "2     {'alogp': '1.85', 'aromatic_rings': 1, 'cx_log...   \n",
              "3     {'alogp': '1.54', 'aromatic_rings': 2, 'cx_log...   \n",
              "4     {'alogp': '1.42', 'aromatic_rings': 2, 'cx_log...   \n",
              "5     {'alogp': '3.93', 'aromatic_rings': 3, 'cx_log...   \n",
              "...                                                 ...   \n",
              "4188  {'alogp': '3.04', 'aromatic_rings': 1, 'cx_log...   \n",
              "4189                                               None   \n",
              "4190  {'alogp': '-1.92', 'aromatic_rings': 0, 'cx_lo...   \n",
              "4191  {'alogp': None, 'aromatic_rings': None, 'cx_lo...   \n",
              "4192  {'alogp': None, 'aromatic_rings': None, 'cx_lo...   \n",
              "\n",
              "                                    molecule_structures    molecule_type  \\\n",
              "0     {'canonical_smiles': 'COc1cc2nc(N3CCN(C(=O)c4c...   Small molecule   \n",
              "2     {'canonical_smiles': 'CN1CCC[C@H]1c1cccnc1', '...   Small molecule   \n",
              "3     {'canonical_smiles': 'CC1COc2c(N3CCN(C)CC3)c(F...   Small molecule   \n",
              "4     {'canonical_smiles': 'CCn1cc(C(=O)O)c(=O)c2ccc...   Small molecule   \n",
              "5     {'canonical_smiles': 'COc1ccc2c(c1)c(CC(=O)O)c...   Small molecule   \n",
              "...                                                 ...              ...   \n",
              "4188  {'canonical_smiles': 'COc1ccc(C(CN(C)C)C2(O)CC...   Small molecule   \n",
              "4189                                               None  Oligonucleotide   \n",
              "4190  {'canonical_smiles': 'Cl.Cl.Cl.Cl.NCCNCCNCCN',...          Unknown   \n",
              "4191                                               None   Small molecule   \n",
              "4192  {'canonical_smiles': 'CC(N)Cc1ccccc1.C[C@H](N)...   Small molecule   \n",
              "\n",
              "       oral  parenteral                      pref_name  topical  \\\n",
              "0      True       False                       PRAZOSIN    False   \n",
              "2      True       False                       NICOTINE     True   \n",
              "3      True        True                      OFLOXACIN     True   \n",
              "4      True       False                 NALIDIXIC ACID    False   \n",
              "5      True        True                   INDOMETHACIN     True   \n",
              "...     ...         ...                            ...      ...   \n",
              "4188   True       False           VENLAFAXINE BESYLATE    False   \n",
              "4189  False        True              INCLISIRAN SODIUM    False   \n",
              "4190  False       False   TRIENTINE TETRAHYDROCHLORIDE    False   \n",
              "4191  False        True                   PSMA-HBED-CC    False   \n",
              "4192   True       False  AMPHETAMINE/DEXTROAMPHETAMINE    False   \n",
              "\n",
              "                                       canonical_smiles  \n",
              "0         COc1cc2nc(N3CCN(C(=O)c4ccco4)CC3)nc(N)c2cc1OC  \n",
              "2                                  CN1CCC[C@H]1c1cccnc1  \n",
              "3      CC1COc2c(N3CCN(C)CC3)c(F)cc3c(=O)c(C(=O)O)cn1c23  \n",
              "4                       CCn1cc(C(=O)O)c(=O)c2ccc(C)nc21  \n",
              "5        COc1ccc2c(c1)c(CC(=O)O)c(C)n2C(=O)c1ccc(Cl)cc1  \n",
              "...                                                 ...  \n",
              "4188  COc1ccc(C(CN(C)C)C2(O)CCCCC2)cc1.O=S(=O)(O)c1c...  \n",
              "4189                                                NaN  \n",
              "4190                             Cl.Cl.Cl.Cl.NCCNCCNCCN  \n",
              "4191                                                NaN  \n",
              "4192  CC(N)Cc1ccccc1.C[C@H](N)Cc1ccccc1.C[C@H](N)Cc1...  \n",
              "\n",
              "[4192 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-335dfd1a-920e-4c6e-b942-c96b2415d1e9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>first_approval</th>\n",
              "      <th>indication_class</th>\n",
              "      <th>max_phase</th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>molecule_properties</th>\n",
              "      <th>molecule_structures</th>\n",
              "      <th>molecule_type</th>\n",
              "      <th>oral</th>\n",
              "      <th>parenteral</th>\n",
              "      <th>pref_name</th>\n",
              "      <th>topical</th>\n",
              "      <th>canonical_smiles</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1976.0</td>\n",
              "      <td>Antihypertensive</td>\n",
              "      <td>4.0</td>\n",
              "      <td>CHEMBL2</td>\n",
              "      <td>{'alogp': '1.78', 'aromatic_rings': 3, 'cx_log...</td>\n",
              "      <td>{'canonical_smiles': 'COc1cc2nc(N3CCN(C(=O)c4c...</td>\n",
              "      <td>Small molecule</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>PRAZOSIN</td>\n",
              "      <td>False</td>\n",
              "      <td>COc1cc2nc(N3CCN(C(=O)c4ccco4)CC3)nc(N)c2cc1OC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1984.0</td>\n",
              "      <td>Smoking Cessation Adjunct</td>\n",
              "      <td>4.0</td>\n",
              "      <td>CHEMBL3</td>\n",
              "      <td>{'alogp': '1.85', 'aromatic_rings': 1, 'cx_log...</td>\n",
              "      <td>{'canonical_smiles': 'CN1CCC[C@H]1c1cccnc1', '...</td>\n",
              "      <td>Small molecule</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>NICOTINE</td>\n",
              "      <td>True</td>\n",
              "      <td>CN1CCC[C@H]1c1cccnc1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1990.0</td>\n",
              "      <td>Antibacterial</td>\n",
              "      <td>4.0</td>\n",
              "      <td>CHEMBL4</td>\n",
              "      <td>{'alogp': '1.54', 'aromatic_rings': 2, 'cx_log...</td>\n",
              "      <td>{'canonical_smiles': 'CC1COc2c(N3CCN(C)CC3)c(F...</td>\n",
              "      <td>Small molecule</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>OFLOXACIN</td>\n",
              "      <td>True</td>\n",
              "      <td>CC1COc2c(N3CCN(C)CC3)c(F)cc3c(=O)c(C(=O)O)cn1c23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1964.0</td>\n",
              "      <td>Antibacterial</td>\n",
              "      <td>4.0</td>\n",
              "      <td>CHEMBL5</td>\n",
              "      <td>{'alogp': '1.42', 'aromatic_rings': 2, 'cx_log...</td>\n",
              "      <td>{'canonical_smiles': 'CCn1cc(C(=O)O)c(=O)c2ccc...</td>\n",
              "      <td>Small molecule</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>NALIDIXIC ACID</td>\n",
              "      <td>False</td>\n",
              "      <td>CCn1cc(C(=O)O)c(=O)c2ccc(C)nc21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1965.0</td>\n",
              "      <td>Anti-Inflammatory</td>\n",
              "      <td>4.0</td>\n",
              "      <td>CHEMBL6</td>\n",
              "      <td>{'alogp': '3.93', 'aromatic_rings': 3, 'cx_log...</td>\n",
              "      <td>{'canonical_smiles': 'COc1ccc2c(c1)c(CC(=O)O)c...</td>\n",
              "      <td>Small molecule</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>INDOMETHACIN</td>\n",
              "      <td>True</td>\n",
              "      <td>COc1ccc2c(c1)c(CC(=O)O)c(C)n2C(=O)c1ccc(Cl)cc1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4188</th>\n",
              "      <td>2022.0</td>\n",
              "      <td>None</td>\n",
              "      <td>4.0</td>\n",
              "      <td>CHEMBL5095051</td>\n",
              "      <td>{'alogp': '3.04', 'aromatic_rings': 1, 'cx_log...</td>\n",
              "      <td>{'canonical_smiles': 'COc1ccc(C(CN(C)C)C2(O)CC...</td>\n",
              "      <td>Small molecule</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>VENLAFAXINE BESYLATE</td>\n",
              "      <td>False</td>\n",
              "      <td>COc1ccc(C(CN(C)C)C2(O)CCCCC2)cc1.O=S(=O)(O)c1c...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4189</th>\n",
              "      <td>2021.0</td>\n",
              "      <td>None</td>\n",
              "      <td>4.0</td>\n",
              "      <td>CHEMBL5095052</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>Oligonucleotide</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>INCLISIRAN SODIUM</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4190</th>\n",
              "      <td>2022.0</td>\n",
              "      <td>None</td>\n",
              "      <td>4.0</td>\n",
              "      <td>CHEMBL5095420</td>\n",
              "      <td>{'alogp': '-1.92', 'aromatic_rings': 0, 'cx_lo...</td>\n",
              "      <td>{'canonical_smiles': 'Cl.Cl.Cl.Cl.NCCNCCNCCN',...</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>TRIENTINE TETRAHYDROCHLORIDE</td>\n",
              "      <td>False</td>\n",
              "      <td>Cl.Cl.Cl.Cl.NCCNCCNCCN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4191</th>\n",
              "      <td>2020.0</td>\n",
              "      <td>None</td>\n",
              "      <td>4.0</td>\n",
              "      <td>CHEMBL5095496</td>\n",
              "      <td>{'alogp': None, 'aromatic_rings': None, 'cx_lo...</td>\n",
              "      <td>None</td>\n",
              "      <td>Small molecule</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>PSMA-HBED-CC</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>4192</th>\n",
              "      <td>2015.0</td>\n",
              "      <td>None</td>\n",
              "      <td>4.0</td>\n",
              "      <td>CHEMBL5095505</td>\n",
              "      <td>{'alogp': None, 'aromatic_rings': None, 'cx_lo...</td>\n",
              "      <td>{'canonical_smiles': 'CC(N)Cc1ccccc1.C[C@H](N)...</td>\n",
              "      <td>Small molecule</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>AMPHETAMINE/DEXTROAMPHETAMINE</td>\n",
              "      <td>False</td>\n",
              "      <td>CC(N)Cc1ccccc1.C[C@H](N)Cc1ccccc1.C[C@H](N)Cc1...</td>\n",
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          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "farmaco=farmaco[['molecule_chembl_id','pref_name','canonical_smiles','molecule_type','max_phase' ,\n",
        "                 'oral','parenteral','topical','first_approval','indication_class']]\n",
        "farmaco"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "i5V8vdOC8Ugh",
        "outputId": "7e5cf6e8-0d0e-4e4e-ecdd-05e91ad25552"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     molecule_chembl_id                      pref_name  \\\n",
              "0               CHEMBL2                       PRAZOSIN   \n",
              "2               CHEMBL3                       NICOTINE   \n",
              "3               CHEMBL4                      OFLOXACIN   \n",
              "4               CHEMBL5                 NALIDIXIC ACID   \n",
              "5               CHEMBL6                   INDOMETHACIN   \n",
              "...                 ...                            ...   \n",
              "4188      CHEMBL5095051           VENLAFAXINE BESYLATE   \n",
              "4189      CHEMBL5095052              INCLISIRAN SODIUM   \n",
              "4190      CHEMBL5095420   TRIENTINE TETRAHYDROCHLORIDE   \n",
              "4191      CHEMBL5095496                   PSMA-HBED-CC   \n",
              "4192      CHEMBL5095505  AMPHETAMINE/DEXTROAMPHETAMINE   \n",
              "\n",
              "                                       canonical_smiles    molecule_type  \\\n",
              "0         COc1cc2nc(N3CCN(C(=O)c4ccco4)CC3)nc(N)c2cc1OC   Small molecule   \n",
              "2                                  CN1CCC[C@H]1c1cccnc1   Small molecule   \n",
              "3      CC1COc2c(N3CCN(C)CC3)c(F)cc3c(=O)c(C(=O)O)cn1c23   Small molecule   \n",
              "4                       CCn1cc(C(=O)O)c(=O)c2ccc(C)nc21   Small molecule   \n",
              "5        COc1ccc2c(c1)c(CC(=O)O)c(C)n2C(=O)c1ccc(Cl)cc1   Small molecule   \n",
              "...                                                 ...              ...   \n",
              "4188  COc1ccc(C(CN(C)C)C2(O)CCCCC2)cc1.O=S(=O)(O)c1c...   Small molecule   \n",
              "4189                                                NaN  Oligonucleotide   \n",
              "4190                             Cl.Cl.Cl.Cl.NCCNCCNCCN          Unknown   \n",
              "4191                                                NaN   Small molecule   \n",
              "4192  CC(N)Cc1ccccc1.C[C@H](N)Cc1ccccc1.C[C@H](N)Cc1...   Small molecule   \n",
              "\n",
              "     max_phase   oral  parenteral  topical  first_approval  \\\n",
              "0          4.0   True       False    False          1976.0   \n",
              "2          4.0   True       False     True          1984.0   \n",
              "3          4.0   True        True     True          1990.0   \n",
              "4          4.0   True       False    False          1964.0   \n",
              "5          4.0   True        True     True          1965.0   \n",
              "...        ...    ...         ...      ...             ...   \n",
              "4188       4.0   True       False    False          2022.0   \n",
              "4189       4.0  False        True    False          2021.0   \n",
              "4190       4.0  False       False    False          2022.0   \n",
              "4191       4.0  False        True    False          2020.0   \n",
              "4192       4.0   True       False    False          2015.0   \n",
              "\n",
              "               indication_class  \n",
              "0              Antihypertensive  \n",
              "2     Smoking Cessation Adjunct  \n",
              "3                 Antibacterial  \n",
              "4                 Antibacterial  \n",
              "5             Anti-Inflammatory  \n",
              "...                         ...  \n",
              "4188                       None  \n",
              "4189                       None  \n",
              "4190                       None  \n",
              "4191                       None  \n",
              "4192                       None  \n",
              "\n",
              "[4192 rows x 10 columns]"
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              "      <th></th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>pref_name</th>\n",
              "      <th>canonical_smiles</th>\n",
              "      <th>molecule_type</th>\n",
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              "      <th>oral</th>\n",
              "      <th>parenteral</th>\n",
              "      <th>topical</th>\n",
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              "      <th>indication_class</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL2</td>\n",
              "      <td>PRAZOSIN</td>\n",
              "      <td>COc1cc2nc(N3CCN(C(=O)c4ccco4)CC3)nc(N)c2cc1OC</td>\n",
              "      <td>Small molecule</td>\n",
              "      <td>4.0</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1976.0</td>\n",
              "      <td>Antihypertensive</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL3</td>\n",
              "      <td>NICOTINE</td>\n",
              "      <td>CN1CCC[C@H]1c1cccnc1</td>\n",
              "      <td>Small molecule</td>\n",
              "      <td>4.0</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>1984.0</td>\n",
              "      <td>Smoking Cessation Adjunct</td>\n",
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              "      <th>3</th>\n",
              "      <td>CHEMBL4</td>\n",
              "      <td>OFLOXACIN</td>\n",
              "      <td>CC1COc2c(N3CCN(C)CC3)c(F)cc3c(=O)c(C(=O)O)cn1c23</td>\n",
              "      <td>Small molecule</td>\n",
              "      <td>4.0</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>1990.0</td>\n",
              "      <td>Antibacterial</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL5</td>\n",
              "      <td>NALIDIXIC ACID</td>\n",
              "      <td>CCn1cc(C(=O)O)c(=O)c2ccc(C)nc21</td>\n",
              "      <td>Small molecule</td>\n",
              "      <td>4.0</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1964.0</td>\n",
              "      <td>Antibacterial</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>CHEMBL6</td>\n",
              "      <td>INDOMETHACIN</td>\n",
              "      <td>COc1ccc2c(c1)c(CC(=O)O)c(C)n2C(=O)c1ccc(Cl)cc1</td>\n",
              "      <td>Small molecule</td>\n",
              "      <td>4.0</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>1965.0</td>\n",
              "      <td>Anti-Inflammatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4188</th>\n",
              "      <td>CHEMBL5095051</td>\n",
              "      <td>VENLAFAXINE BESYLATE</td>\n",
              "      <td>COc1ccc(C(CN(C)C)C2(O)CCCCC2)cc1.O=S(=O)(O)c1c...</td>\n",
              "      <td>Small molecule</td>\n",
              "      <td>4.0</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4189</th>\n",
              "      <td>CHEMBL5095052</td>\n",
              "      <td>INCLISIRAN SODIUM</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Oligonucleotide</td>\n",
              "      <td>4.0</td>\n",
              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>2021.0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4190</th>\n",
              "      <td>CHEMBL5095420</td>\n",
              "      <td>TRIENTINE TETRAHYDROCHLORIDE</td>\n",
              "      <td>Cl.Cl.Cl.Cl.NCCNCCNCCN</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>4.0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4191</th>\n",
              "      <td>CHEMBL5095496</td>\n",
              "      <td>PSMA-HBED-CC</td>\n",
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              "      <td>4.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>2020.0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4192</th>\n",
              "      <td>CHEMBL5095505</td>\n",
              "      <td>AMPHETAMINE/DEXTROAMPHETAMINE</td>\n",
              "      <td>CC(N)Cc1ccccc1.C[C@H](N)Cc1ccccc1.C[C@H](N)Cc1...</td>\n",
              "      <td>Small molecule</td>\n",
              "      <td>4.0</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4192 rows × 10 columns</p>\n",
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              "  </style>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "farmacos_3 = molecula.filter(max_phase=3\n",
        "                                #atc_classifications__level2='L04', #Clasificación ATC de inmunosupresores\n",
        "                                ).only(['oral','parenteral','topical', 'molecule_properties'#'black_box_warning',\n",
        "                                        'canonical_smiles','first_approval','indication_class', 'molecule_structures'\n",
        "                                        'max_phase','pref_name','molecule_type', 'molecule_chembl_id'])\n",
        "farmacos_3= pd.DataFrame.from_records(farmacos_3)\n",
        "farmacos_3['canonical_smiles'] = farmacos_3.loc[ farmacos_3['molecule_structures'].notnull(), 'molecule_structures'].apply(lambda x: x['canonical_smiles']) #This is the mwt of the parent compound\n",
        "print(farmacos_3.info())\n",
        "farmacos_3=farmacos_3.drop_duplicates(subset='molecule_chembl_id', keep='first')\n",
        "print(farmacos_3.info())\n",
        "farmacos_3[0:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 984
        },
        "id": "dWxkqdxAjPGs",
        "outputId": "1b6791a6-c9c7-40ec-aed0-bc411c8237de"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1736 entries, 0 to 1735\n",
            "Data columns (total 12 columns):\n",
            " #   Column               Non-Null Count  Dtype \n",
            "---  ------               --------------  ----- \n",
            " 0   first_approval       0 non-null      object\n",
            " 1   indication_class     174 non-null    object\n",
            " 2   max_phase            1736 non-null   object\n",
            " 3   molecule_chembl_id   1736 non-null   object\n",
            " 4   molecule_properties  1128 non-null   object\n",
            " 5   molecule_structures  1105 non-null   object\n",
            " 6   molecule_type        1736 non-null   object\n",
            " 7   oral                 1736 non-null   bool  \n",
            " 8   parenteral           1736 non-null   bool  \n",
            " 9   pref_name            1736 non-null   object\n",
            " 10  topical              1736 non-null   bool  \n",
            " 11  canonical_smiles     1105 non-null   object\n",
            "dtypes: bool(3), object(9)\n",
            "memory usage: 127.3+ KB\n",
            "None\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1735 entries, 0 to 1735\n",
            "Data columns (total 12 columns):\n",
            " #   Column               Non-Null Count  Dtype \n",
            "---  ------               --------------  ----- \n",
            " 0   first_approval       0 non-null      object\n",
            " 1   indication_class     174 non-null    object\n",
            " 2   max_phase            1735 non-null   object\n",
            " 3   molecule_chembl_id   1735 non-null   object\n",
            " 4   molecule_properties  1127 non-null   object\n",
            " 5   molecule_structures  1104 non-null   object\n",
            " 6   molecule_type        1735 non-null   object\n",
            " 7   oral                 1735 non-null   bool  \n",
            " 8   parenteral           1735 non-null   bool  \n",
            " 9   pref_name            1735 non-null   object\n",
            " 10  topical              1735 non-null   bool  \n",
            " 11  canonical_smiles     1104 non-null   object\n",
            "dtypes: bool(3), object(9)\n",
            "memory usage: 140.6+ KB\n",
            "None\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  first_approval indication_class max_phase molecule_chembl_id  \\\n",
              "0           None             None       3.0       CHEMBL204021   \n",
              "2           None   Antineoplastic       3.0         CHEMBL7301   \n",
              "\n",
              "                                 molecule_properties  \\\n",
              "0  {'alogp': '7.22', 'aromatic_rings': 4, 'cx_log...   \n",
              "2  {'alogp': '6.86', 'aromatic_rings': 1, 'cx_log...   \n",
              "\n",
              "                                 molecule_structures   molecule_type   oral  \\\n",
              "0  {'canonical_smiles': 'CCN(CC)CCN(Cc1ccc(-c2ccc...  Small molecule  False   \n",
              "2  {'canonical_smiles': 'CC1=C(/C=C/C(C)=C/C=C/C(...  Small molecule  False   \n",
              "\n",
              "   parenteral    pref_name  topical  \\\n",
              "0       False   DARAPLADIB    False   \n",
              "2       False  FENRETINIDE    False   \n",
              "\n",
              "                                    canonical_smiles  \n",
              "0  CCN(CC)CCN(Cc1ccc(-c2ccc(C(F)(F)F)cc2)cc1)C(=O...  \n",
              "2  CC1=C(/C=C/C(C)=C/C=C/C(C)=C/C(=O)Nc2ccc(O)cc2...  "
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-065377cd-729b-4901-95ee-3610f5cdc4d6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "farmacos_3=farmacos_3[['molecule_chembl_id','pref_name','canonical_smiles','molecule_type','max_phase' ,\n",
        "                 'oral','parenteral','topical','first_approval','indication_class']]\n",
        "farmacos_3[0:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "id": "qEWii8ONjseR",
        "outputId": "b7cd53ac-b4b8-4245-a501-afc62e63f5c9"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  molecule_chembl_id    pref_name  \\\n",
              "0       CHEMBL204021   DARAPLADIB   \n",
              "2         CHEMBL7301  FENRETINIDE   \n",
              "\n",
              "                                    canonical_smiles   molecule_type  \\\n",
              "0  CCN(CC)CCN(Cc1ccc(-c2ccc(C(F)(F)F)cc2)cc1)C(=O...  Small molecule   \n",
              "2  CC1=C(/C=C/C(C)=C/C=C/C(C)=C/C(=O)Nc2ccc(O)cc2...  Small molecule   \n",
              "\n",
              "  max_phase   oral  parenteral  topical first_approval indication_class  \n",
              "0       3.0  False       False    False           None             None  \n",
              "2       3.0  False       False    False           None   Antineoplastic  "
            ],
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              "      <th>molecule_chembl_id</th>\n",
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              "      <th>canonical_smiles</th>\n",
              "      <th>molecule_type</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>CHEMBL204021</td>\n",
              "      <td>DARAPLADIB</td>\n",
              "      <td>CCN(CC)CCN(Cc1ccc(-c2ccc(C(F)(F)F)cc2)cc1)C(=O...</td>\n",
              "      <td>Small molecule</td>\n",
              "      <td>3.0</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL7301</td>\n",
              "      <td>FENRETINIDE</td>\n",
              "      <td>CC1=C(/C=C/C(C)=C/C=C/C(C)=C/C(=O)Nc2ccc(O)cc2...</td>\n",
              "      <td>Small molecule</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>None</td>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-06db9942-6c0c-4206-b221-faa41d607510');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_concatenado = pd.concat([farmaco, farmacos_3], axis=0)\n",
        "print(df_concatenado.info())\n",
        "\n",
        "df_small_molecules = df_concatenado.copy()\n",
        "df_small_molecules = df_small_molecules[df_small_molecules['molecule_type'] == 'Small molecule']\n",
        "print(df_small_molecules.info())\n",
        "df_small_molecules.head()"
      ],
      "metadata": {
        "id": "YrDn8pPH8Uj-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 952
        },
        "outputId": "ee439173-c56d-443c-ca86-71daaeedb86e"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 5927 entries, 0 to 1735\n",
            "Data columns (total 10 columns):\n",
            " #   Column              Non-Null Count  Dtype \n",
            "---  ------              --------------  ----- \n",
            " 0   molecule_chembl_id  5927 non-null   object\n",
            " 1   pref_name           5927 non-null   object\n",
            " 2   canonical_smiles    4596 non-null   object\n",
            " 3   molecule_type       5927 non-null   object\n",
            " 4   max_phase           5927 non-null   object\n",
            " 5   oral                5927 non-null   bool  \n",
            " 6   parenteral          5927 non-null   bool  \n",
            " 7   topical             5927 non-null   bool  \n",
            " 8   first_approval      3215 non-null   object\n",
            " 9   indication_class    2414 non-null   object\n",
            "dtypes: bool(3), object(7)\n",
            "memory usage: 387.8+ KB\n",
            "None\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 4702 entries, 0 to 1735\n",
            "Data columns (total 10 columns):\n",
            " #   Column              Non-Null Count  Dtype \n",
            "---  ------              --------------  ----- \n",
            " 0   molecule_chembl_id  4702 non-null   object\n",
            " 1   pref_name           4702 non-null   object\n",
            " 2   canonical_smiles    4424 non-null   object\n",
            " 3   molecule_type       4702 non-null   object\n",
            " 4   max_phase           4702 non-null   object\n",
            " 5   oral                4702 non-null   bool  \n",
            " 6   parenteral          4702 non-null   bool  \n",
            " 7   topical             4702 non-null   bool  \n",
            " 8   first_approval      2758 non-null   object\n",
            " 9   indication_class    2169 non-null   object\n",
            "dtypes: bool(3), object(7)\n",
            "memory usage: 307.7+ KB\n",
            "None\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "  molecule_chembl_id       pref_name  \\\n",
              "0            CHEMBL2        PRAZOSIN   \n",
              "2            CHEMBL3        NICOTINE   \n",
              "3            CHEMBL4       OFLOXACIN   \n",
              "4            CHEMBL5  NALIDIXIC ACID   \n",
              "5            CHEMBL6    INDOMETHACIN   \n",
              "\n",
              "                                   canonical_smiles   molecule_type max_phase  \\\n",
              "0     COc1cc2nc(N3CCN(C(=O)c4ccco4)CC3)nc(N)c2cc1OC  Small molecule       4.0   \n",
              "2                              CN1CCC[C@H]1c1cccnc1  Small molecule       4.0   \n",
              "3  CC1COc2c(N3CCN(C)CC3)c(F)cc3c(=O)c(C(=O)O)cn1c23  Small molecule       4.0   \n",
              "4                   CCn1cc(C(=O)O)c(=O)c2ccc(C)nc21  Small molecule       4.0   \n",
              "5    COc1ccc2c(c1)c(CC(=O)O)c(C)n2C(=O)c1ccc(Cl)cc1  Small molecule       4.0   \n",
              "\n",
              "   oral  parenteral  topical first_approval           indication_class  \n",
              "0  True       False    False         1976.0           Antihypertensive  \n",
              "2  True       False     True         1984.0  Smoking Cessation Adjunct  \n",
              "3  True        True     True         1990.0              Antibacterial  \n",
              "4  True       False    False         1964.0              Antibacterial  \n",
              "5  True        True     True         1965.0          Anti-Inflammatory  "
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>pref_name</th>\n",
              "      <th>canonical_smiles</th>\n",
              "      <th>molecule_type</th>\n",
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              "      <th>oral</th>\n",
              "      <th>parenteral</th>\n",
              "      <th>topical</th>\n",
              "      <th>first_approval</th>\n",
              "      <th>indication_class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL2</td>\n",
              "      <td>PRAZOSIN</td>\n",
              "      <td>COc1cc2nc(N3CCN(C(=O)c4ccco4)CC3)nc(N)c2cc1OC</td>\n",
              "      <td>Small molecule</td>\n",
              "      <td>4.0</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1976.0</td>\n",
              "      <td>Antihypertensive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL3</td>\n",
              "      <td>NICOTINE</td>\n",
              "      <td>CN1CCC[C@H]1c1cccnc1</td>\n",
              "      <td>Small molecule</td>\n",
              "      <td>4.0</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>1984.0</td>\n",
              "      <td>Smoking Cessation Adjunct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL4</td>\n",
              "      <td>OFLOXACIN</td>\n",
              "      <td>CC1COc2c(N3CCN(C)CC3)c(F)cc3c(=O)c(C(=O)O)cn1c23</td>\n",
              "      <td>Small molecule</td>\n",
              "      <td>4.0</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>1990.0</td>\n",
              "      <td>Antibacterial</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL5</td>\n",
              "      <td>NALIDIXIC ACID</td>\n",
              "      <td>CCn1cc(C(=O)O)c(=O)c2ccc(C)nc21</td>\n",
              "      <td>Small molecule</td>\n",
              "      <td>4.0</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1964.0</td>\n",
              "      <td>Antibacterial</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>CHEMBL6</td>\n",
              "      <td>INDOMETHACIN</td>\n",
              "      <td>COc1ccc2c(c1)c(CC(=O)O)c(C)n2C(=O)c1ccc(Cl)cc1</td>\n",
              "      <td>Small molecule</td>\n",
              "      <td>4.0</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>1965.0</td>\n",
              "      <td>Anti-Inflammatory</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-48632e78-9b8c-4d3f-8ba3-c9bb060a0d96')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-0872b1ea-c6ec-4505-ae12-c5fac70ad11c\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0872b1ea-c6ec-4505-ae12-c5fac70ad11c')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-0872b1ea-c6ec-4505-ae12-c5fac70ad11c button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-48632e78-9b8c-4d3f-8ba3-c9bb060a0d96 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-48632e78-9b8c-4d3f-8ba3-c9bb060a0d96');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_app=df_small_molecules.copy()\n",
        "df_app=df_app.dropna(subset='first_approval')\n",
        "print(df_app.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VuJ7clJlpWak",
        "outputId": "7eb92d0d-c060-4d61-ddb3-e420c18932f5"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 2758 entries, 0 to 4192\n",
            "Data columns (total 10 columns):\n",
            " #   Column              Non-Null Count  Dtype \n",
            "---  ------              --------------  ----- \n",
            " 0   molecule_chembl_id  2758 non-null   object\n",
            " 1   pref_name           2758 non-null   object\n",
            " 2   canonical_smiles    2628 non-null   object\n",
            " 3   molecule_type       2758 non-null   object\n",
            " 4   max_phase           2758 non-null   object\n",
            " 5   oral                2758 non-null   bool  \n",
            " 6   parenteral          2758 non-null   bool  \n",
            " 7   topical             2758 non-null   bool  \n",
            " 8   first_approval      2758 non-null   object\n",
            " 9   indication_class    1671 non-null   object\n",
            "dtypes: bool(3), object(7)\n",
            "memory usage: 180.5+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_app=df_app.drop_duplicates(subset='molecule_chembl_id')\n",
        "df_app = df_app.reset_index(drop=True)\n",
        "print(df_app.info())\n",
        "df_app[0:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "LD6Nzb9loe7n",
        "outputId": "1c3ebeb1-3529-4dc3-832f-8ed7ba694bc5"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2758 entries, 0 to 2757\n",
            "Data columns (total 10 columns):\n",
            " #   Column              Non-Null Count  Dtype \n",
            "---  ------              --------------  ----- \n",
            " 0   molecule_chembl_id  2758 non-null   object\n",
            " 1   pref_name           2758 non-null   object\n",
            " 2   canonical_smiles    2628 non-null   object\n",
            " 3   molecule_type       2758 non-null   object\n",
            " 4   max_phase           2758 non-null   object\n",
            " 5   oral                2758 non-null   bool  \n",
            " 6   parenteral          2758 non-null   bool  \n",
            " 7   topical             2758 non-null   bool  \n",
            " 8   first_approval      2758 non-null   object\n",
            " 9   indication_class    1671 non-null   object\n",
            "dtypes: bool(3), object(7)\n",
            "memory usage: 159.0+ KB\n",
            "None\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  molecule_chembl_id pref_name                               canonical_smiles  \\\n",
              "0            CHEMBL2  PRAZOSIN  COc1cc2nc(N3CCN(C(=O)c4ccco4)CC3)nc(N)c2cc1OC   \n",
              "1            CHEMBL3  NICOTINE                           CN1CCC[C@H]1c1cccnc1   \n",
              "\n",
              "    molecule_type max_phase  oral  parenteral  topical first_approval  \\\n",
              "0  Small molecule       4.0  True       False    False         1976.0   \n",
              "1  Small molecule       4.0  True       False     True         1984.0   \n",
              "\n",
              "            indication_class  \n",
              "0           Antihypertensive  \n",
              "1  Smoking Cessation Adjunct  "
            ],
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              "\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>pref_name</th>\n",
              "      <th>canonical_smiles</th>\n",
              "      <th>molecule_type</th>\n",
              "      <th>max_phase</th>\n",
              "      <th>oral</th>\n",
              "      <th>parenteral</th>\n",
              "      <th>topical</th>\n",
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              "      <th>indication_class</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>CHEMBL2</td>\n",
              "      <td>PRAZOSIN</td>\n",
              "      <td>COc1cc2nc(N3CCN(C(=O)c4ccco4)CC3)nc(N)c2cc1OC</td>\n",
              "      <td>Small molecule</td>\n",
              "      <td>4.0</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1976.0</td>\n",
              "      <td>Antihypertensive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL3</td>\n",
              "      <td>NICOTINE</td>\n",
              "      <td>CN1CCC[C@H]1c1cccnc1</td>\n",
              "      <td>Small molecule</td>\n",
              "      <td>4.0</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>1984.0</td>\n",
              "      <td>Smoking Cessation Adjunct</td>\n",
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              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8e8b8e17-4733-4591-9807-1379dcc6d5fe')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
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              "\n",
              "\n",
              "    <div id=\"df-378c9d1f-9ae3-4736-8afe-8973b5026aaa\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-378c9d1f-9ae3-4736-8afe-8973b5026aaa')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-378c9d1f-9ae3-4736-8afe-8973b5026aaa button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8e8b8e17-4733-4591-9807-1379dcc6d5fe button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8e8b8e17-4733-4591-9807-1379dcc6d5fe');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_app.to_excel('chembl_search_output.xlsx') #Guardar dataframe en un archivo tipo excel"
      ],
      "metadata": {
        "id": "IKSrLCVEq93r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Referencias y otras ligas de interés:**\n",
        "1. [Servidor web de ChEMBL](https://www.ebi.ac.uk/chembl/)\n",
        "2. [Esquema de ChEMBL](https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/latest/chembl_33_schema.png)\n",
        "3. [Adquisición de datos de ChEMBL de TeachOpenCADD(consultarlo para mayor información)](https://projects.volkamerlab.org/teachopencadd/talktorials/T001_query_chembl.html)\n",
        "4. [Script de obtención de información de ChEMBL por Leela S. Dodda](https://gist.github.com/leelasd/746f3c447805fc6ea5aad1071bd77aef)\n",
        "5. [Documentación de la API de ChEMBL](https://www.ebi.ac.uk/chembl/api/utils/docs)\n",
        "6. [Otros ejemplos del uso de la API](https://github.com/chembl/notebooks/blob/main/ChEMBL_API_example_for_webinar.ipynb)\n"
      ],
      "metadata": {
        "id": "dd0ENP_G3Jo8"
      }
    }
  ]
}