Pharmacokinetic (PK) behavior, which emerges from the underlying processes of absorption, distribution, metabolism, and excretion (ADME), is central to drug discovery and development, dose optimization, and safety assessment. Despite decades of experimental and computational research, early-stage prediction of human PK remains a major challenge, contributing to clinical attrition and inefficiency in pharmaceutical pipelines. Advances in artificial intelligence (AI) and machine learning (ML) have significantly improved ADME predictions, particularly for small molecules. Traditional descriptor-based quantitative structure-activity relationship and classical ML methods offer interpretability and robust performance on standardized datasets. In contrast, graph neural networks, deep learning architectures, and chemical language models facilitate the learning of complex nonlinear structure-property relationships and multitask predictions. Multimodal frameworks further integrate experimental measurements, structural data, and biological contexts, enhancing predictive accuracy under low-data and heterogeneous conditions. Emerging modalities, including peptides, oligonucleotides, and antibody-based therapeutics, pose additional challenges owing to their sequence-dependent stability, conformational flexibility, and mechanistically distinct determinants of ADME and toxicity (ADMET). AI approaches that incorporate sequence-, structure-, and mechanism-aware representations combined with multimodal data integration have demonstrated improved predictability for medium- and large-molecule therapeutics. Recent developments in foundation-model architectures offer unified representations across chemical, biological, and biophysical domains, enabling cross-modality ADMET modeling with enhanced generalization and mechanistic interpretability. In this review, we summarize the evolution of computational ADME- and PK-oriented prediction frameworks from small molecules to complex biologics, highlighting methodological advances, representative studies, and emerging trends in multimodal and foundation-model approaches. We also discuss the limitations and future perspectives of the practical implementation of AI-driven ADMET predictions to support rational drug design and development.
Tanihata et al. (Thu,) studied this question.