Chemical Structure-based Graph Convolutional Model for Drug-Gut Microbiota Association Prediction

doi:10.1007/s40242-025-5068-y

Abstract

Abstract: The gut microbiota plays a crucial role in modulating drug metabolism, efficacy, and toxicity. However, experimental strategies heavily suffered from the technic challenges of the isolation and in vitro culture of individual microbiota species. Predicting gut microbiota-drug associations (GutMDA) is therefore essential for advancing microbiome-informed pharmacology. In this study, we proposed a graph convolutional network-based model GutMDA that utilizes chemical structure similarity for drug representation, and integrates gut microbiota and disease information to enable efficient and accurate prediction of drug-microbiota associations. Benchmarking results on curated datasets show superior predictive performance compared to existing approaches. Additionally, the case studies showed that more than 90 percent of top 20 predicted associations have been validated experimentally in recent publications, which further demonstrates the accuracy of GutMDA. In a word, GutMDA provided an effective and interpretable tool for gut microbiota-drug associations prediction.

Key words: Graph convolutional network, Gut microbiota, Drug-microbiota association, Precision medicine

WANG Shuaiqi, LI Xingxiu, ZHOU Kaicheng, LI Jianxi, HOU Dongyue, XU Caili, YANG Yuan, JU Dianwen ZENG Xian. Chemical Structure-based Graph Convolutional Model for Drug-Gut Microbiota Association Prediction[J]. Chemical Research in Chinese Universities, 2025, 41(4): 983-991.

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