让药物相互作用预测说出理由，提升可信度
ExDDI: Explaining Drug-Drug Interaction Predictions with Natural Language
- 用自然语言生成预测背后的药理机制
- 在已知药物间预测未知相互作用并解释
- 适合临床用药安全研究者使用
预测未知药物相互作用（DDI）对提升用药安全至关重要。以往的DDI预测工作多聚焦于二分类或类别预测，缺乏可解释性，难以建立用户信任。本文提出生成自然语言解释的DDI预测方法，使模型在做出预测的同时，揭示其背后的药效学与药代动力学机制。我们从DDInter和DrugBank收集了DDI解释数据，并构建多种模型进行实验与分析。结果表明，所提模型能为已知药物间的未知相互作用提供准确解释。本研究为DDI预测领域提供了新工具，并为生成可解释性预测奠定了基础。
原文摘要 · Abstract (English)
Predicting unknown drug-drug interactions (DDIs) is crucial for improving medication safety. Previous efforts in DDI prediction have typically focused on binary classification or predicting DDI categories, with the absence of explanatory insights that could enhance trust in these predictions. In this work, we propose to generate natural language explanations for DDI predictions, enabling the model to reveal the underlying pharmacodynamics and pharmacokinetics mechanisms simultaneously as making the prediction. To do this, we have collected DDI explanations from DDInter and DrugBank and developed various models for extensive experiments and analysis. Our models can provide accurate explanations for unknown DDIs between known drugs. This paper contributes new tools to the field of DDI prediction and lays a solid foundation for further research on generating explanations for DDI predictions.
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