Feldmann C, Bajorath J. Calculation of Exact Shapley Values for Support Vector Machines with Tanimoto Kernel Enables Model Interpretation.
iScience 2022;
25:105023. [PMID:
36105596 PMCID:
PMC9464958 DOI:
10.1016/j.isci.2022.105023]
[Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 08/09/2022] [Accepted: 08/20/2022] [Indexed: 11/24/2022] Open
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.
SVETA: new methodology for explaining support vector machine (SVM) predictions
Tanimoto similarity-based SVM models are popular in chemistry
SVETA enables the calculation of exact Shapley values for rationalizing SVM models
SVETA-based feature mapping provides intuitive explanations of SVM decisions
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