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.

本研究针对药物-药物相互作用（DDI）预测中的解释性不足问题，提出了一种生成自然语言解释的创新方法。这种方法不仅可以提高对DDI预测的信任度，还揭示了基础的药效学和药代动力学机制。研究表明，所开发的模型能够为已知药物之间的未知DDI提供准确的解释，对DDI预测领域的工具开发具有重要贡献。

ExDDI：利用自然语言解释药物-药物相互作用预测