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.

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Publication Details

Journal
Journal of Medicinal Chemistry
Published
2026-09-18
DOI
https://doi.org/10.1021/acs.jmedchem.6c02126
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

Contrastive Learning for Metabolite-Aware Oral Drug Design

Kangdong Liu, Shuai He, Feng Hu, T Ye et al.
Journal of Medicinal Chemistry
Computational Drug Discovery Methods
article

Contrastive Learning for Metabolite-Aware Oral Drug Design

Kangdong Liu, Shuai He, Feng Hu, T Ye, Yanru Yang, Manzhan Zhang, Honglin Li, Xiayu Shi, Xuhong Qian, Zhenjiang Zhao, Shiliang Li, Xingsen Zhang, Leihao Zhang, Na Chen, Huan He, Xiaoxiao Yang, Rong Zhang, Kai Zhang, Xiaoqian Zhu, Chang Liu, Zhuo Chen, Zhe Wang, Rui Wang
article en

Abstract

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.

Journal of Medicinal Chemistry
East China University of Science and Technology (CN), Zhengzhou University (CN), Baotou Medical College (CN), East China Normal University (CN), Air Force Medical University (CN)
National Natural Science Foundation of China, Science and Technology Commission of Shanghai Municipality, Shanghai Rising-Star Program, Shaanxi Provincial Health Commission, National Key Research and Development Program of China, Key Research and Development Projects of Shaanxi Province
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Openalex Percentile: Top 9%
Computational Drug Discovery Methods
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