Zhang_2026_Front.Mol.Biosci_13_1854162

Traditional Chinese Medicine constitutes a chemically diverse and pharmacologically rich reservoir of bioactive compounds, often exhibiting multi-target pharmacological properties that are potentially valuable for complex metabolic disorders such as type 2 diabetes mellitus (T2DM). However, systematic prioritization of active and safe constituents remains challenging, as therapeutic efficacy must be optimized concurrently with toxicity risk. Here, we present a safety-aware Multi-Task Graph Attention Network (SM-GAT) framework that jointly models anti-diabetic efficacy and toxicity liabilities of TCM-derived compounds within a unified architecture. Four tasks are simultaneously optimized: inhibition of dipeptidyl peptidase-4 (DPP4), inhibition of alpha-glucosidase, acute oral toxicity, and clinically relevant toxicity. By enabling shared molecular representation learning across heterogeneous yet biologically related endpoints, SM-GAT facilitates knowledge transfer between efficacy and safety domains. Across all prediction tasks, SM-GAT achieved competitive or superior performance compared with single-task graph neural networks and other baseline models, achieving ROC-AUC values up to 0.892 for alpha-glucosidase inhibition. Notably, multi-task learning yields pronounced improvements in data-limited settings, highlighting effective cross-task regularization. Large-scale virtual screening of the TCMBank library demonstrates practical applicability, enabling efficient prioritization of structurally diverse candidates with favorable predicted efficacy-safety balance. Several structurally diverse lead compounds, including ellagic acid derivatives, are identified with favorable predicted efficacy-safety balance. Furthermore, atom-level attention analysis highlighted chemically interpretable substructures associated with predicted efficacy and toxicity-related molecular representations. Collectively, this study establishes an interpretable multi-objective framework for safety-aware lead discovery, providing a computational framework for integrating traditional botanical resources into anti-diabetic lead discovery.