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<p>The rapid emergence of combination pharmacotherapies offers substantial therapeutic advantages but also poses risks of adverse drug reactions (ADRs). The accurate prediction of ADRs with interpretable computational methods is crucial for clinical medication management, drug development and precision medicine. Machine-learning and recently developed deep learning architectures struggle to effectively elucidate the key protein–protein interactions underlying ADRs from an organ perspective and to explicitly represent ADR associations. Here we propose OrganADR, an associative learning-enhanced model to predict ADRs at the organ level for emerging combination pharmacotherapy. It incorporates ADR information at the organ level, drug information at the molecular level and network-based biomedical knowledge into integrated representations with multi-interpretable modules. Evaluation across 15 organs demonstrates that OrganADR not only achieves state-of-the-art performance but also delivers both interpretable insights at the organ level and network-based perspectives. Overall, OrganADR represents a useful tool for cross-scale biomedical information integration and could be used to prevent ADRs during clinical precision medicine.</p>

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Predicting adverse drug reactions for combination pharmacotherapy with cross-scale associative learning via attention modules

  • 期刊论文

  • 发表日期：

    2025年6月30日

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  • Boyang Li,
  • Yifan Qi,
  • Bo Li,
  • Xiaoqiong Li

摘要

The rapid emergence of combination pharmacotherapies offers substantial therapeutic advantages but also poses risks of adverse drug reactions (ADRs). The accurate prediction of ADRs with interpretable computational methods is crucial for clinical medication management, drug development and precision medicine. Machine-learning and recently developed deep learning architectures struggle to effectively elucidate the key protein–protein interactions underlying ADRs from an organ perspective and to explicitly represent ADR associations. Here we propose OrganADR, an associative learning-enhanced model to predict ADRs at the organ level for emerging combination pharmacotherapy. It incorporates ADR information at the organ level, drug information at the molecular level and network-based biomedical knowledge into integrated representations with multi-interpretable modules. Evaluation across 15 organs demonstrates that OrganADR not only achieves state-of-the-art performance but also delivers both interpretable insights at the organ level and network-based perspectives. Overall, OrganADR represents a useful tool for cross-scale biomedical information integration and could be used to prevent ADRs during clinical precision medicine.