2020/05/02 by Raquel Rodríguez-Pérez, Jürgen Bajorath · 5 citations
Computer Science · Materials Science · #Computational Drug Discovery Methods #Machine Learning in Materials Science
paper · pdf · doi:10.1007/s10822-020-00314-0
openalex publication_date 2020/05/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/03
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.