Contrastive Learning for Metabolite-Aware Oral Drug Design
Huan He, Manzhan Zhang, Xiaoxiao Yang, Shuai He, Xiayu Shi, Feng Hu, Chang Liu, Xingsen Zhang, Na Chen, Xiaoqian Zhu, Leihao Zhang, Tianyu Ye, Rong Zhang, Yanru Yang, Rui Wang, Zhenjiang Zhao, Zhuo Chen, Xuhong H. Qian, Honglin Li, Zhe Wang · Journal of Medicinal Chemistry · 2026
Abstract Unfavorable drug metabolism drives clinical failure, limited by the low accuracy of existing AI predictors. To address this, we constructed a database of 11,665 human-specific reactions and developed Mettle, a novel AI model integrating chemical feature interaction with contrastive learning. By explicitly training the model to distinguish true metabolic transformations from structurally similar decoys, Mettle achieves state-of-the-art performance with ∼80% top-5 accuracy. We demonstrate Mettle’s utility by tackling poor oral bioavailability in RSK4 inhibitors. This metabolite-aware design strategy yielded R636, which maintains high potency while exhibiting a remarkable 63-fold increase in absolute bioavailability (to 63%). R636 showed a favorable safety profile and significant antitumor efficacy in two ESCC PDX models. Mettle thus emerges as a powerful tool for metabolite-aware oral drug design.