ArticleiScience2022
Calculation of exact Shapley values for support vector machines with Tanimoto kernel enables model interpretation.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Chemically Interpretable Explanations for Molecular Property Prediction via Fragment-Level Shapley Values.Journal of chemical information and modeling · 2026Article
- Reliable Apparent Permeability Prediction Through Benchmarking, Stability, Uncertainty Quantification, and Explainable Machine Learning.CPT: pharmacometrics & systems pharmacology · 2026Article
- DriverSub-SVM: a machine learning approach for cancer subtype classification by integrating patient-specific and global driver genes.BMC bioinformatics · 2025Article
- Improving Machine Learning Classification Predictions through SHAP and Features Analysis Interpretation.Journal of chemical information and modeling · 2025Article
- Comparing Explanations of Molecular Machine Learning Models Generated with Different Methods for the Calculation of Shapley Values.Molecular informatics · 2025Article
- Calculation of exact Shapley values for explaining support vector machine models using the radial basis function kernel.Scientific reports · 2023Article
- Explaining compound activity predictions with a substructure-aware loss for graph neural networks.Journal of cheminformatics · 2023Article
- Machine learning to predict bacteriologic confirmation of Mycobacterium tuberculosis in infants and very young children.PLOS digital health · 2023Article
- Differences in learning characteristics between support vector machine and random forest models for compound classification revealed by Shapley value analysis.Scientific reports · 2023Article
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The support vector machine (SVM) algorithm is popular in chemistry and drug discovery. SVM models have black box character. Their predictions can be interpreted through feature weighting or the model-agnostic Shapley additive explanations (SHAP) formalism that locally approximates Shapley values (SVs) originating from game theory. We introduce an algorithm termed SV-expressed Tanimoto similarity (SVETA) for the exact calculation of SVs to explain SVM models employing the Tanimoto kernel, the gold standard for the assessment of molecular similarity. For a model system, the exact calculation of SVs is demonstrated. In an SVM-based compound classification task from drug discovery, only a limited correlation between exact SV and SHAP values is observed, prohibiting the use of approximate values for rationalizing predictions. For exemplary test compounds, atom-based mapping of prioritized features delineates coherent substructures that closely resemble those obtained by analyzing independently derived random forest models, thus providing consistent explanations.
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