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

arXiv: https://arxiv.org/abs/2407.13291 · HTML: https://mlsanity.com/paper/2407.13291/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 94 references that are in the map.
- First posted on arXiv 18 Jul 2024.

## Builds on (most cited first, 6 of 6)

- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [MoleculeNet: A Benchmark for Molecular Machine Learning](https://mlsanity.com/paper/1703.00564/) (2017)
- [Tunability: Importance of Hyperparameters of Machine Learning Algorithms](https://mlsanity.com/paper/1802.09596/) (2018)
- [Molecular Property Prediction Based on Graph Structure Learning](https://mlsanity.com/paper/2312.16855/) (2023)
- [Molecular Topological Profile (MOLTOP) -- Simple and Strong Baseline for Molecular Graph Classification](https://mlsanity.com/paper/2407.12136/) (2024)
- [ApisTox: a new benchmark dataset for the classification of small molecules toxicity on honey bees](https://mlsanity.com/paper/2404.16196/) (2024)

## Built on by (most cited first, 9 of 9)

- [Benchmarking Pretrained Molecular Embedding Models For Molecular Representation Learning](https://mlsanity.com/paper/2508.06199/) (2025)
- [Molecular Fingerprints Are Strong Models for Peptide Function Prediction](https://mlsanity.com/paper/2501.17901/) (2025)
- [ChemOrch: Empowering LLMs with Chemical Intelligence via Synthetic Instructions](https://mlsanity.com/paper/2509.16543/) (2025)
- [Evaluating machine learning models for predicting pesticide toxicity to honey bees](https://mlsanity.com/paper/2503.24305/) (2025)
- [MolPILE -- large-scale, diverse dataset for molecular representation learning](https://mlsanity.com/paper/2509.18353/) (2025)
- [Towards Rational Pesticide Design with Graph Machine Learning Models for Ecotoxicology](https://mlsanity.com/paper/2509.18703/) (2025)
- [Barlow Twins Deep Neural Network for Advanced 1D Drug-Target Interaction Prediction](https://mlsanity.com/paper/2408.00040/) (2024)
- [MolLIBRA: Genetic Molecular Optimization with Multi-Fingerprint Surrogates and Text-Molecule Aligned Critic](https://mlsanity.com/paper/2602.07002/) (2026)
- [Scikit-fingerprints: Python library for scikit-learn compatible molecular fingerprints and chemoinformatics](https://mlsanity.com/paper/2608.02027/) (2026)

## Abstract (arXiv)

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.