Contrastive Learning for Metabolite-Aware Oral Drug Design

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.