Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions

Difficulties in interpreting machine learning (ML) models and their predictions limit the practical applicability of and confidence in ML in pharmaceutical research. There is a need for agnostic approaches aiding in the interpretation of ML models regardless of their complexity that is also applicab...

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Published inJournal of computer-aided molecular design Vol. 34; no. 10; pp. 1013 - 1026
Main Authors Rodríguez-Pérez, Raquel, Bajorath, Jürgen
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 01.10.2020
Springer Nature B.V
Subjects
Animal Anatomy
Artificial neural networks
Chemistry
Chemistry and Materials Science
Computer Applications in Chemistry
Decision trees
Histology
Machine learning
Morphology
Physical Chemistry
Regression analysis
Regression models
Feature importance
Shapley values
Multi-target modeling
Compound activity
Machine learning
Black box character
Model interpretation
Structure–activity relationships
Compound potency prediction
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Summary:Difficulties in interpreting machine learning (ML) models and their predictions limit the practical applicability of and confidence in ML in pharmaceutical research. There is a need for agnostic approaches aiding in the interpretation of ML models regardless of their complexity that is also applicable to deep neural network (DNN) architectures and model ensembles. To these ends, the SHapley Additive exPlanations (SHAP) methodology has recently been introduced. The SHAP approach enables the identification and prioritization of features that determine compound classification and activity prediction using any ML model. Herein, we further extend the evaluation of the SHAP methodology by investigating a variant for exact calculation of Shapley values for decision tree methods and systematically compare this variant in compound activity and potency value predictions with the model-independent SHAP method. Moreover, new applications of the SHAP analysis approach are presented including interpretation of DNN models for the generation of multi-target activity profiles and ensemble regression models for potency prediction.
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ISSN:0920-654X
1573-4951
DOI:10.1007/s10822-020-00314-0