SM-GAT: a safety-aware multi-task graph attention network for multi-target anti-diabetic lead discovery from natural products

Abstract

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 α-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 α-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.