mlsanityData to 7 Oct 2026

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

arXiv 2608.02027 ↗First posted 3 Aug 2026cs.LG cs.SEcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 82 references that are in the map. First posted on arXiv 3 Aug 2026.

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References · 17

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
  3. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  4. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,037
  5. 2021Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Developmentcited by 258
  6. 2022Long Range Graph Benchmarkcited by 258
  7. 2016Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learningcited by 193
  8. 2017Accelerated Hierarchical Density Clusteringcited by 100
  9. 2023Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Languagecited by 44
  10. 2025Benchmarking Pretrained Molecular Embedding Models For Molecular Representation Learningcited by 15
  11. 2025Deep Learning Foundation Models from Classical Molecular Descriptorscited by 11
  12. 2024Scikit-fingerprints: easy and efficient computation of molecular fingerprints in Pythoncited by 9
5 more
  1. 2024Molecular Topological Profile (MOLTOP) -- Simple and Strong Baseline for Molecular Graph Classificationcited by 7
  2. 2024ApisTox: a new benchmark dataset for the classification of small molecules toxicity on honey beescited by 6
  3. 2025Molecular Fingerprints Are Strong Models for Peptide Function Predictioncited by 5
  4. 2025Evaluating machine learning models for predicting pesticide toxicity to honey beescited by 2
  5. 2025Towards Rational Pesticide Design with Graph Machine Learning Models for Ecotoxicologycited by 1

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