  In this work, we present \textit{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, \textit{scikit-fingerprints} stands as the
most feature-rich library in the 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.
