Abstract
Predicting drug metabolism remains a long-standing challenge in pharmacokinetics due to the mechanistic complexity of enzymatic transformations and the fragmented nature of current computational tools. Existing models are typically limited to isolated tasks - substrate recognition, metabolic site identification, or metabolite generation - lacking mechanistic fidelity, holistic integration, and chemical interpretability. Here, we introduce DeepMetab, the first comprehensive and mechanistically informed deep graph learning framework for end-to-end prediction of CYP450-mediated drug metabolism. DeepMetab uniquely integrates three essential prediction tasks - substrate profiling, site-of-metabolism (SOM) localization, and metabolite generation - within a unified multi-task architecture. It employs a dual-labeling strategy that simultaneously captures atom- and bond-level reactivity, and infuses multi-scale features including quantum-informed and topological descriptors into a graph neural network (GNN) backbone. A curated knowledge base of expert-derived reaction rules further ensures mechanistic consistency during metabolite synthesis. DeepMetab consistently outperformed existing models across nine major CYP isoforms in all three prediction tasks. Its strong generalizability was further validated on 18 recently FDA-approved drugs, achieving 100% TOP-2 accuracy for SOM prediction and accurately recovering several experimentally confirmed metabolites absent from the training set. Visualization of learned representations reveals expert-level discernment of electronic characteristics, steric architecture, and regiochemical determinants, underscoring the model's interpretability. Together, DeepMetab represents a next-generation AI system that bridges symbolic reaction rules and deep graph reasoning to deliver accurate, interpretable, and end-to-end metabolism predictions, offering tangible value for both preclinical research and regulatory applications.