Integration of Machine Learning With PBPK and QSAR Modeling Approaches to Facilitate Drug Discovery and Development

This review examines the application of machine learning (ML) in physiologically based pharmacokinetic (PBPK) modeling through improved prediction of input parameters, particularly via quantitative structure-activity relationship (QSAR) models, for absorption, distribution, metabolism, and excretion (ADME) properties across drug development phases. Traditional PBPK models, while mechanistically sound, face limitations in applicability domain when compound-specific physicochemical and biochemical