# Scikit-fingerprints: Python library for scikit-learn compatible molecular fingerprints and chemoinformatics

arXiv: https://arxiv.org/abs/2608.02027 · HTML: https://mlsanity.com/paper/2608.02027/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 82 references that are in the map.
- First posted on arXiv 3 Aug 2026.

## Builds on (most cited first, 17 of 17)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [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)
- [Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development](https://mlsanity.com/paper/2102.09548/) (2021)
- [Long Range Graph Benchmark](https://mlsanity.com/paper/2206.08164/) (2022)
- [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016)
- [Accelerated Hierarchical Density Clustering](https://mlsanity.com/paper/1705.07321/) (2017)
- [Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language](https://mlsanity.com/paper/2303.03363/) (2023)
- [Benchmarking Pretrained Molecular Embedding Models For Molecular Representation Learning](https://mlsanity.com/paper/2508.06199/) (2025)
- [Deep Learning Foundation Models from Classical Molecular Descriptors](https://mlsanity.com/paper/2506.15792/) (2025)
- [Scikit-fingerprints: easy and efficient computation of molecular fingerprints in Python](https://mlsanity.com/paper/2407.13291/) (2024)
- [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)
- [Molecular Fingerprints Are Strong Models for Peptide Function Prediction](https://mlsanity.com/paper/2501.17901/) (2025)
- [Evaluating machine learning models for predicting pesticide toxicity to honey bees](https://mlsanity.com/paper/2503.24305/) (2025)
- [Towards Rational Pesticide Design with Graph Machine Learning Models for Ecotoxicology](https://mlsanity.com/paper/2509.18703/) (2025)

## Abstract (arXiv)

We present scikit-fingerprints, a comprehensive, fully scikit-learn compatible library for molecular machine learning in Python, based on RDKit. Molecular fingerprints and related functionalities are workhorses of chemoinformatics, yet the widely used open-source frameworks are not compatible with the wider Python machine learning ecosystem based on scikit-learn conventions. scikit-fingerprints closes this gap, bringing molecular fingerprints, molecular filters, similarity and distance measures, applicability domain estimation, data splitting strategies, and more under a single, familiar interface. Scikit-learn compatibility means that an entire chemoinformatics workflow, from a raw SMILES string to a deployable model, can be assembled from composable building blocks and can reuse the mature tooling of the surrounding ecosystem. The underlying RDKit code makes it familiar and extensible for custom chemoinformatics use cases. We put a strong focus on unified interfaces, ease of use, computational efficiency, customization, and extensibility. scikit-fingerprints makes molecular machine learning faster to prototype, easier to reproduce, and simpler to deploy.