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Molecules (Basel, Switzerland)|July 29, 2023
Explaining Multiclass Compound Activity Predictions Using Counterfactuals and Shapley ValuesAlec Lamens, Jürgen BajorathJournal of Cheminformatics|September 23, 2025
Contrastive explanations for machine learning predictions in chemistryAlec Lamens, Jürgen BajorathRSC Medicinal Chemistry|May 24, 2024
Systematic generation and analysis of counterfactuals for compound activity predictions using multi-task modelsAlec Lamens, Jürgen BajorathChemical Science|January 14, 2026
Explainable artificial intelligence for molecular design in pharmaceutical researchAlec Lamens, Jürgen BajorathJournal of Chemical Information and Modeling|January 17, 2025
Rationalizing Predictions of Isoform-Selective Phosphoinositide 3-Kinase Inhibitors Using MolAnchor AnalysisAlec Lamens, Jürgen BajorathMolecular Informatics|March 20, 2025
Comparing Explanations of Molecular Machine Learning Models Generated with Different Methods for the Calculation of Shapley ValuesAlec Lamens, Jürgen BajorathChemmedchem|November 20, 2023
Generation of Molecular Counterfactuals for Explainable Machine Learning Based on Core-Substituent RecombinationAlec Lamens, Jürgen BajorathMolecules (Basel, Switzerland)|January 21, 2023
Explaining Accurate Predictions of Multitarget Compounds with Machine Learning Models Derived for Individual TargetsAlec Lamens, Jürgen BajorathExpert Opinion on Drug Discovery|March 20, 2013
Computational approaches in chemogenomics and chemical biology: current and future impact on drug discoveryJürgen BajorathPageof 54