Multitask learning in predictive toxicology: methods, benchmarks, and applications

Abstract

ABSTRACT Introduction Drug safety evaluation is inherently multi-endpoint, multi-scale, and mechanistically interconnected. Traditional predictive toxicology approaches rely heavily on single endpoint models, limiting their ability to leverage shared biological and chemical determinants across assays, species, and clinical outcomes. Multitask learning (MTL), a machine learning paradigm in which multiple endpoints are learned jointly through shared representations, offers a unifying strategy for integrating metabolism, mechanistic toxicity, and clinical risk prediction. Areas covered This review examines the theoretical foundations and practical implementation of MTL in predictive toxicology and drug metabolism. We evaluate benchmark datasets (Tox21, ToxCast, ClinTox), ADME–Tox integration strategies, mechanistically informed task grouping, transfer learning, and validation frameworks. Applications to drug-induced liver injury, cardiotoxicity, multispecies toxicity, and early discovery optimization are discussed. Regulatory considerations and interpretability challenges are also analyzed. Expert opinion MTL reframes predictive toxicology from endpoint-specific modeling toward integrated safety representation learning. When mechanistically grounded and rigorously validated, MTL can improve predictive robustness, data efficiency, and translational alignment. Its future impact in drug metabolism and toxicology will depend on harmonized datasets, explainability frameworks, and regulatory engagement.