QSAR_KRAS_Machine_Learning_Model_to_Predict_Bioactivity.ipynb

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            "Collecting rdkit\n",
            "  Downloading rdkit-2024.3.6-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (4.0 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from rdkit) (1.26.4)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from rdkit) (11.0.0)\n",
            "Downloading rdkit-2024.3.6-cp310-cp310-manylinux_2_28_x86_64.whl (32.8 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m32.8/32.8 MB\u001b[0m \u001b[31m22.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: rdkit\n",
            "Successfully installed rdkit-2024.3.6\n"
          ]
        }
      ],
      "source": [
        "!pip install rdkit"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import Descriptors\n",
        "from rdkit.Chem import rdMolDescriptors\n",
        "from rdkit.Chem import Descriptors, rdMolDescriptors, AllChem\n",
        "from rdkit.DataStructs import cDataStructs\n",
        "from sklearn.feature_selection import VarianceThreshold\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestRegressor"
      ],
      "metadata": {
        "id": "b8PRqLLs_Yqj"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Upload Bioactivity data (pIC50) of molecules (smiles)**"
      ],
      "metadata": {
        "id": "-yofM7AdavI8"
      }
    },
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        "id": "_11WHIiRTOag",
        "outputId": "b1188bb9-6fee-4ca1-fabb-3bd9ce8b8c12"
      },
      "outputs": [
        {
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              "                                               Smiles     pIC50\n",
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              "1                        Cc1[nH]c2cc(Cl)cc(Cl)c2c1CCN  3.809668\n",
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              "4   CCn1c(=O)c(-c2cc(NC(=O)NCCC(C)(C)C)c(F)cc2C)cc...  6.920819\n",
              "..                                                ...       ...\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 81,\n  \"fields\": [\n    {\n      \"column\": \"Smiles\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 81,\n        \"samples\": [\n          \"Cn1ccnc1CCOc1nc(N2C[C@H]3CC[C@@H](C2)N3)c2cnc(-c3cc(O)cc4ccccc34)c(F)c2n1\",\n          \"CCCS(=O)(=O)Nc1ccc(F)c(C(=O)c2c[nH]c3ncc(-c4ccc(Cl)cc4)cc23)c1F\",\n          \"CC(C)C[C@H](NC(=O)[C@@H]1COCc2cnnn2CCCCCC(=O)N2CCC[C@H]2C(=O)N[C@@H](Cc2cccc3ccccc23)C(=O)N1)C(N)=O\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pIC50\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5648274213261868,\n        \"min\": 3.809668302,\n        \"max\": 10.0,\n        \"num_unique_values\": 68,\n        \"samples\": [\n          5.920818754,\n          9.22184875,\n          6.920818754\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "\n",
        "df = pd.read_csv('Bioactivity_Data_clean.csv')\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.dropna ()\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "uBPbjwxHR57i",
        "outputId": "ac15ac0a-82b9-4d1c-a840-d07fcd2f3ed3"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               Smiles     pIC50\n",
              "0   CCCS(=O)(=O)Nc1ccc(F)c(C(=O)c2c[nH]c3ncc(-c4cc...  4.779892\n",
              "1                        Cc1[nH]c2cc(Cl)cc(Cl)c2c1CCN  3.809668\n",
              "2   N#Cc1ccc2c(c1)CN(S(=O)(=O)c1cccs1)[C@H](Cc1ccc...  5.638272\n",
              "3   CCn1c(=O)c(-c2ccc(F)c(NC(=O)Nc3cc(C(C)(C)C)no3...  7.040959\n",
              "4   CCn1c(=O)c(-c2cc(NC(=O)NCCC(C)(C)C)c(F)cc2C)cc...  6.920819\n",
              "..                                                ...       ...\n",
              "76  Cc1cc(N)nc(-c2cc3nc(OC[C@@H]4CCCN4C)nc4c3c(c2C...  5.619789\n",
              "77  Cc1cc(N)nc(-c2cc3nc(OC[C@@H]4CCCN4C)nc4c3c(c2C...  5.086186\n",
              "78  Cc1cc(N)nc(-c2cc3nc(OC[C@@H]4CCCN4C)nc4c3c(c2C...  5.677781\n",
              "79  Cc1cc(N)nc(-c2cc3nc(OC[C@@H]4CCCN4C)nc4c3c(c2C...  5.619789\n",
              "80  O=C(Cc1ccncc1)N1CCN([C@H]2c3ccc(Cl)cc3CCc3cccn...  8.283997\n",
              "\n",
              "[81 rows x 2 columns]"
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              "      <th>Smiles</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CCCS(=O)(=O)Nc1ccc(F)c(C(=O)c2c[nH]c3ncc(-c4cc...</td>\n",
              "      <td>4.779892</td>\n",
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              "      <th>1</th>\n",
              "      <td>Cc1[nH]c2cc(Cl)cc(Cl)c2c1CCN</td>\n",
              "      <td>3.809668</td>\n",
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              "      <th>2</th>\n",
              "      <td>N#Cc1ccc2c(c1)CN(S(=O)(=O)c1cccs1)[C@H](Cc1ccc...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CCn1c(=O)c(-c2ccc(F)c(NC(=O)Nc3cc(C(C)(C)C)no3...</td>\n",
              "      <td>7.040959</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CCn1c(=O)c(-c2cc(NC(=O)NCCC(C)(C)C)c(F)cc2C)cc...</td>\n",
              "      <td>6.920819</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>Cc1cc(N)nc(-c2cc3nc(OC[C@@H]4CCCN4C)nc4c3c(c2C...</td>\n",
              "      <td>5.619789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>Cc1cc(N)nc(-c2cc3nc(OC[C@@H]4CCCN4C)nc4c3c(c2C...</td>\n",
              "      <td>5.086186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>Cc1cc(N)nc(-c2cc3nc(OC[C@@H]4CCCN4C)nc4c3c(c2C...</td>\n",
              "      <td>5.677781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>Cc1cc(N)nc(-c2cc3nc(OC[C@@H]4CCCN4C)nc4c3c(c2C...</td>\n",
              "      <td>5.619789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>O=C(Cc1ccncc1)N1CCN([C@H]2c3ccc(Cl)cc3CCc3cccn...</td>\n",
              "      <td>8.283997</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 81,\n  \"fields\": [\n    {\n      \"column\": \"Smiles\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 81,\n        \"samples\": [\n          \"Cn1ccnc1CCOc1nc(N2C[C@H]3CC[C@@H](C2)N3)c2cnc(-c3cc(O)cc4ccccc34)c(F)c2n1\",\n          \"CCCS(=O)(=O)Nc1ccc(F)c(C(=O)c2c[nH]c3ncc(-c4ccc(Cl)cc4)cc23)c1F\",\n          \"CC(C)C[C@H](NC(=O)[C@@H]1COCc2cnnn2CCCCCC(=O)N2CCC[C@H]2C(=O)N[C@@H](Cc2cccc3ccccc23)C(=O)N1)C(N)=O\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pIC50\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5648274213261868,\n        \"min\": 3.809668302,\n        \"max\": 10.0,\n        \"num_unique_values\": 68,\n        \"samples\": [\n          5.920818754,\n          9.22184875,\n          6.920818754\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**FingerPrint & 2D Descriptors**"
      ],
      "metadata": {
        "id": "fNm5EtLXq6tP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from rdkit.Chem.rdmolops import PatternFingerprint\n",
        "from rdkit.Chem import rdFingerprintGenerator\n",
        "\n",
        "\n",
        "def predict_bioactivity(smiles):\n",
        "    m = Chem.MolFromSmiles(smiles)\n",
        "    desc = []\n",
        "\n",
        "    # Calculate atom pair fingerprint using AtomPairGenerator\n",
        "    fp_generator = rdFingerprintGenerator.GetAtomPairGenerator()\n",
        "    fp = fp_generator.GetFingerprint(m)\n",
        "    desc.extend([int(x) for x in fp.ToBitString()])\n",
        "\n",
        "    # Calculate RDK fingerprint\n",
        "    fp = Chem.RDKFingerprint(m)\n",
        "    desc.extend(fp)\n",
        "\n",
        "    # Calculate additional molecular descriptors\n",
        "    desc.append(Descriptors.MolWt(m))\n",
        "    desc.append(Descriptors.MolLogP(m))\n",
        "    desc.append(rdMolDescriptors.CalcNumHBA(m))\n",
        "    desc.append(rdMolDescriptors.CalcNumHBD(m))\n",
        "    desc.append(Descriptors.TPSA(m))\n",
        "    desc.append(rdMolDescriptors.CalcFractionCSP3(m))\n",
        "    desc.append(rdMolDescriptors.CalcNumRotatableBonds(m))\n",
        "    desc.append(rdMolDescriptors.CalcNumAromaticRings(m))\n",
        "    desc.append(rdMolDescriptors.CalcNumHeterocycles(m))\n",
        "\n",
        "    return desc"
      ],
      "metadata": {
        "id": "TtSiBVHpAbb5"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert the descriptors list to a Pandas dataframe\n",
        "df2 = pd.DataFrame(descriptors)\n",
        "df2.to_csv('decriptors.csv', index=False)\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "t28RUVm9BeYI",
        "outputId": "bd2f6bb5-3a16-47e3-8417-2d424808a365"
      },
      "execution_count": 30,
      "outputs": [
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-834b8c0d-5059-4d25-a94c-a540a9e2ac06 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_abff6c3f-0026-4b86-a338-ee276ea8259a\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df2')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_abff6c3f-0026-4b86-a338-ee276ea8259a button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df2');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df2"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df2\n",
        "y = df[\"pIC50\"]"
      ],
      "metadata": {
        "id": "X7OLkX8dB692"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Split data into training and test sets**"
      ],
      "metadata": {
        "id": "MeevgZsdF82z"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "vMnx3rK8ZTui"
      },
      "outputs": [],
      "source": [
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xz5safLBeuQu"
      },
      "source": [
        "# **QSAR Modell RandomForestRegressor**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "fmhzzkRtDg8h",
        "outputId": "e6a23694-124c-48e8-c986-45e06303d638"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-3 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-3 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-3 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-3 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-3 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(random_state=42)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "# train QSAR model\n",
        "model = RandomForestRegressor(random_state=42)\n",
        "model.fit(X, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "ng_u1LU_DlfJ"
      },
      "outputs": [],
      "source": [
        "# predict bioactivity on test set\n",
        "y_pred = model.predict(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8dnObJB7DpF6",
        "outputId": "b63c780c-aa11-4796-a9e0-95b4d6a03b97"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R^2: 0.9735494323593402\n"
          ]
        }
      ],
      "source": [
        "# evaluate model performance\n",
        "print(\"R^2:\", model.score(X_test, y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "GN0eLD8JNgKL"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "ngLnAqYKNiNa",
        "outputId": "56b0a05b-f890-4e75-b377-fdef368bc5b5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(5,5))\n",
        "plt.scatter(x=y, y=y_pred, c=\"#7CAE00\", alpha=0.3)\n",
        "\n",
        "z = np.polyfit(y, y_pred, 1)\n",
        "p = np.poly1d(z)\n",
        "\n",
        "plt.plot(y,p(y),\"#F8766D\")\n",
        "plt.ylabel('Predicted pIC50')\n",
        "plt.xlabel('Experimental pIC50')\n",
        "plt.savefig('regression_curve.pdf')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Define a function to calculate the descriptors of a new molecule (SINGLE) and Predict pIC50**"
      ],
      "metadata": {
        "id": "Pqk9p0_5YjPi"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5oLvbSEADylZ",
        "outputId": "191cf243-ac89-4f42-ed72-fb543ea2f4f8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted bioactivity (pIC50): [6.99512555]\n",
            "IC50: [101.12870599] nM\n"
          ]
        }
      ],
      "source": [
        "from rdkit.Chem.rdmolops import PatternFingerprint\n",
        "from rdkit.Chem import rdFingerprintGenerator\n",
        "\n",
        "def predict_bioactivity(smiles):\n",
        "    m = Chem.MolFromSmiles(smiles)\n",
        "    desc = []\n",
        "\n",
        "    # Calculate atom pair fingerprint using AtomPairGenerator\n",
        "    fp_generator = rdFingerprintGenerator.GetAtomPairGenerator()\n",
        "    fp = fp_generator.GetFingerprint(m)\n",
        "    desc.extend([int(x) for x in fp.ToBitString()])\n",
        "\n",
        "    # Calculate RDK fingerprint\n",
        "    fp = Chem.RDKFingerprint(m)\n",
        "    desc.extend(fp)\n",
        "\n",
        "    # Calculate additional molecular descriptors\n",
        "    desc.append(Descriptors.MolWt(m))\n",
        "    desc.append(Descriptors.MolLogP(m))\n",
        "    desc.append(rdMolDescriptors.CalcNumHBA(m))\n",
        "    desc.append(rdMolDescriptors.CalcNumHBD(m))\n",
        "    desc.append(Descriptors.TPSA(m))\n",
        "    desc.append(rdMolDescriptors.CalcFractionCSP3(m))\n",
        "    desc.append(rdMolDescriptors.CalcNumRotatableBonds(m))\n",
        "    desc.append(rdMolDescriptors.CalcNumAromaticRings(m))\n",
        "    desc.append(rdMolDescriptors.CalcNumHeterocycles(m))\n",
        "\n",
        "    return desc\n",
        "\n",
        "# predict bioactivity of new molecule (Sotorasib)\n",
        "smiles = \"C[C@H]1CN(CCN1C2=NC(=O)N(C3=NC(=C(C=C32)F)C4=C(C=CC=C4F)O)C5=C(C=CN=C5C(C)C)C)C(=O)C=C\"\n",
        "X_new = np.array(predict_bioactivity(smiles)).reshape(1, -1)\n",
        "\n",
        "# Print the selected features\n",
        "#print(selected_features)\n",
        "bioactivity = model.predict(X_new)\n",
        "print(\"Predicted bioactivity (pIC50):\", bioactivity)\n",
        "\n",
        "pIC50 = bioactivity\n",
        "IC50 = (10**-pIC50)*(10**9)\n",
        "print(\"IC50:\", IC50, \"nM\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from rdkit.Chem.rdmolops import PatternFingerprint\n",
        "from rdkit.Chem import rdFingerprintGenerator\n",
        "\n",
        "def predict_bioactivity(smiles):\n",
        "    m = Chem.MolFromSmiles(smiles)\n",
        "    desc = []\n",
        "\n",
        "    # Calculate atom pair fingerprint using AtomPairGenerator\n",
        "    fp_generator = rdFingerprintGenerator.GetAtomPairGenerator()\n",
        "    fp = fp_generator.GetFingerprint(m)\n",
        "    desc.extend([int(x) for x in fp.ToBitString()])\n",
        "\n",
        "    # Calculate RDK fingerprint\n",
        "    fp = Chem.RDKFingerprint(m)\n",
        "    desc.extend(fp)\n",
        "\n",
        "    # Calculate additional molecular descriptors\n",
        "    desc.append(Descriptors.MolWt(m))\n",
        "    desc.append(Descriptors.MolLogP(m))\n",
        "    desc.append(rdMolDescriptors.CalcNumHBA(m))\n",
        "    desc.append(rdMolDescriptors.CalcNumHBD(m))\n",
        "    desc.append(Descriptors.TPSA(m))\n",
        "    desc.append(rdMolDescriptors.CalcFractionCSP3(m))\n",
        "    desc.append(rdMolDescriptors.CalcNumRotatableBonds(m))\n",
        "    desc.append(rdMolDescriptors.CalcNumAromaticRings(m))\n",
        "    desc.append(rdMolDescriptors.CalcNumHeterocycles(m))\n",
        "\n",
        "    return desc\n",
        "\n",
        "# predict bioactivity of new molecule (Adagrasib)\n",
        "smiles = \"CN1CCC[C@H]1COC2=NC3=C(CCN(C3)C4=CC=CC5=C4C(=CC=C5)Cl)C(=N2)N6CCN([C@H](C6)CC#N)C(=O)C(=C)F\"\n",
        "X_new = np.array(predict_bioactivity(smiles)).reshape(1, -1)\n",
        "\n",
        "# Print the selected features\n",
        "#print(selected_features)\n",
        "bioactivity = model.predict(X_new)\n",
        "print(\"Predicted bioactivity (pIC50):\", bioactivity)\n",
        "\n",
        "pIC50 = bioactivity\n",
        "IC50 = (10**-pIC50)*(10**9)\n",
        "print(\"IC50:\", IC50, \"nM\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k66F39Z4DtX5",
        "outputId": "6121f366-5bf1-444e-c5d2-7d1c6f1dd0d2"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted bioactivity (pIC50): [7.57895588]\n",
            "IC50: [26.36599243] nM\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Convert pIC50 to IC50 (nm)**"
      ],
      "metadata": {
        "id": "jBLq3xGOZABJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pIC50 = bioactivity\n",
        "IC50 = (10**-pIC50)*(10**9)\n",
        "print(\"IC50:\", IC50, \"nM\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uPw3Tfv9QEpM",
        "outputId": "fb4c59a8-ebc4-475d-eabf-8b4468c9cb62"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "IC50: [1325.46668338] nM\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Define a function to calculate the descriptors of a new set of molecules (smiles.csv) and Predict pIC50 (predicted_ic50.csv).    smiles.csv file should have \"Compound ID\" and \"Smiles\" columns.**"
      ],
      "metadata": {
        "id": "c81sxdi0B_Ph"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from rdkit.Chem.rdmolops import PatternFingerprint\n",
        "from rdkit.Chem import rdFingerprintGenerator\n",
        "\n",
        "def predict_bioactivity(smiles):\n",
        "    m = Chem.MolFromSmiles(smiles)\n",
        "    desc = []\n",
        "\n",
        "    # Calculate atom pair fingerprint using AtomPairGenerator\n",
        "    fp_generator = rdFingerprintGenerator.GetAtomPairGenerator()\n",
        "    fp = fp_generator.GetFingerprint(m)\n",
        "    desc.extend([int(x) for x in fp.ToBitString()])\n",
        "\n",
        "    # Calculate RDK fingerprint\n",
        "    fp = Chem.RDKFingerprint(m)\n",
        "    desc.extend(fp)\n",
        "\n",
        "    # Calculate additional molecular descriptors\n",
        "    desc.append(Descriptors.MolWt(m))\n",
        "    desc.append(Descriptors.MolLogP(m))\n",
        "    desc.append(rdMolDescriptors.CalcNumHBA(m))\n",
        "    desc.append(rdMolDescriptors.CalcNumHBD(m))\n",
        "    desc.append(Descriptors.TPSA(m))\n",
        "    desc.append(rdMolDescriptors.CalcFractionCSP3(m))\n",
        "    desc.append(rdMolDescriptors.CalcNumRotatableBonds(m))\n",
        "    desc.append(rdMolDescriptors.CalcNumAromaticRings(m))\n",
        "    desc.append(rdMolDescriptors.CalcNumHeterocycles(m))\n",
        "\n",
        "    return desc\n",
        "\n",
        "# Function to process a CSV file and predict IC50 for each compound\n",
        "def predict_ic50_from_csv(csv_file, model, output_file):\n",
        "    # Read the CSV file\n",
        "    data = pd.read_csv(csv_file)\n",
        "    smiles_list = data['Smiles']  # Assumes the SMILES column is named 'Smiles'\n",
        "\n",
        "    # Initialize results\n",
        "    results = []\n",
        "\n",
        "    for idx, smiles in enumerate(smiles_list):\n",
        "        try:\n",
        "            # Calculate descriptors\n",
        "            desc = predict_bioactivity(smiles)\n",
        "            X_new = np.array(desc).reshape(1, -1)\n",
        "\n",
        "            # Predict pIC50\n",
        "            pIC50 = model.predict(X_new)[0]  # Assuming model outputs a single value\n",
        "\n",
        "            # Convert pIC50 to IC50 (nM)\n",
        "            IC50 = (10 ** -pIC50) * (10 ** 9)\n",
        "\n",
        "            # Append results\n",
        "            results.append({\n",
        "                'Compound ID': data.loc[idx, 'Compound ID'],  # Assumes a 'Compound ID' column\n",
        "                'SMILES': smiles,\n",
        "                'Predicted pIC50': pIC50,\n",
        "                'Predicted IC50 (nM)': IC50\n",
        "            })\n",
        "        except Exception as e:\n",
        "            print(f\"Error processing SMILES {smiles}: {e}\")\n",
        "            results.append({\n",
        "                'Compound ID': data.loc[idx, 'Compound ID'],\n",
        "                'Smiles': smiles,\n",
        "                'Predicted pIC50': None,\n",
        "                'Predicted IC50 (nM)': None\n",
        "            })\n",
        "\n",
        "    # Save results to a CSV file\n",
        "    results_df = pd.DataFrame(results)\n",
        "    results_df.to_csv(output_file, index=False)\n",
        "    print(f\"Predictions saved to {output_file}\")\n",
        "\n",
        "# Example usage\n",
        "csv_file = \"smiles.csv\"  # Input CSV file\n",
        "output_file = \"predicted_ic50.csv\"  # Output CSV file\n",
        "predict_ic50_from_csv(csv_file, model, output_file)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V3YsiY1_82VB",
        "outputId": "f9ab02cb-4cb0-4408-c4e2-65e077d93bca"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predictions saved to predicted_ic50.csv\n"
          ]
        }
      ]
    }
  ]
}