CITATION.bib

@article{bjerrum_scikit-mol_2023,
	title = {Scikit-{Mol} brings cheminformatics to {Scikit}-{Learn}},
	author = {Bjerrum, Esben Jannik and Bachorz, Rafał Adam and Bitton, Adrien and Choung, Oh-hyeon and Chen, Ya and Esposito, Carmen and Ha, Son Viet and Poehlmann, Andreas},
	year = {2023},
	month = dec,
	journal = {ChemRxiv},
	url = {https://chemrxiv.org/engage/chemrxiv/article-details/60ef0fc58825826143a82cc0},
	doi = {10.26434/chemrxiv-2023-fzqwd},
	abstract = {Scikit-Mol is a open-source toolkit that aims to bridge the gap between two well-established toolkits, RDKit and Scikit-Learn, in order to provide a simple interface for building cheminformatics models. By leveraging the strengths of both RDKit and Scikit-Learn, Scikit-Mol provides a powerful platform for creating predictive modeling in drug discovery and materials design. Unlike other toolkits that often integrate both chemistry and machine learning, Scikit-Mol rather aims to be a simple bridge between the two, reducing the maintenance effort required to keep up with changes and new features in e.g. Scikit-Learn. A simple example of Scikit-Mol's functionality is provided, demonstrating its compatibility with Scikit-Learn pipelines. Overall, Scikit-Mol provides a useful and flexible package for building self-contained and self-documented cheminformatics models with minimal maintenance required.},
	language = {en},
	urldate = {2023-12-06},
	keywords = {Cheminformatics, Descriptors, Fingerprints, Machine Learning, RDKit, Scikit-Learn},
	note = {preprint}
}