Scikit-fingerprints: easy and efficient computation of molecular fingerprints in Python
<|reference_start|>Scikit-fingerprints: easy and efficient computation of molecular fingerprints in Python: In this work, we present \skfp, a Python package for computation of molecular fingerprints for applications in chemoinformatics. Our library offers an industry-standard scikit-learn interface, allowing intuitive usage and easy integration with machine learning pipelines. It is also highly optimized, featuring parallel computation that enables efficient processing of large molecular datasets. Currently, \skfp~stands as the most feature-rich library in the open source Python ecosystem, offering over 30 molecular fingerprints. Our library simplifies chemoinformatics tasks based on molecular fingerprints, including molecular property prediction and virtual screening. It is also flexible, highly efficient, and fully open source.<|reference_end|>
@article{adamczyk2024scikit-fingerprints:,
    title={Scikit-fingerprints: easy and efficient computation of molecular
  fingerprints in Python},
    author={Jakub Adamczyk, Piotr Ludynia},
    journal={arXiv preprint arXiv:2407.13291},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13291},
    primaryClass={cs.SE cs.LG}
}