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<p>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 <i>in vitro</i> 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 drugmicrobiota 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 microbiotadrug associations prediction.</p>

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

  • 期刊论文

  • 发表日期：

    2025年7月8日

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  • Shuaiqi Wang,
  • Xingxiu Li,
  • Kaicheng Zhou,
  • Jianxi Li,
  • Dongyue Hou,
  • Caili Xu,
  • Yuan Yang,
  • Dianwen Ju,
  • Xian Zeng

摘要

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 drugmicrobiota 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 microbiotadrug associations prediction.