Work

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

Year: 2024

Type: software-paper

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.

Source: SoftwareX

DOI: 10.1016/j.softx.2024.101944

Authors: Jakub Adamczyk, Piotr Ludynia

Institution: AGH University of Krakow

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $970

Funders: Ministerstwo Edukacji i Nauki, Akademia Górniczo-Hutnicza im. Stanislawa Staszica

OpenAlex ID: W4403947974

Cites: 73

Cited by: 26

Related to: 10