Scikit-fingerprints: easy and efficient computation of molecular fingerprints in Python
This provides a feature-rich, efficient tool for researchers and practitioners in chemoinformatics, though it is incremental as it builds on existing fingerprint methods with improved usability and integration.
The authors tackled the problem of computing molecular fingerprints for chemoinformatics by developing scikit-fingerprints, a Python package that offers over 30 molecular fingerprints with an industry-standard scikit-learn interface and parallel computation for efficiency.
In this work, we present scikit-fingerprints, 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, scikit-fingerprints 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.