Scikit-fingerprints: easy and efficient computation of molecular fingerprints in Python

TL;DR

scikit-fingerprints is a Python library that simplifies and accelerates the computation of over 30 molecular fingerprints, facilitating chemoinformatics tasks like property prediction and virtual screening with an easy-to-use, scalable interface.

Contribution

The paper introduces scikit-fingerprints, the most feature-rich open-source Python library for molecular fingerprint computation with parallel processing and scikit-learn compatibility.

Findings

Supports over 30 molecular fingerprints

Enables efficient processing of large datasets

Offers seamless integration with machine learning pipelines

Abstract

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.