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.

翻译：预测未知的药物相互作用（DDI）对于提升用药安全至关重要。先前DDI预测研究通常侧重于二元分类或DDI类别预测，缺乏能够增强预测可信度的解释性见解。在本研究中，我们提出为DDI预测生成自然语言解释，使模型在进行预测的同时能够揭示潜在的药效学与药代动力学机制。为此，我们从DDInter和DrugBank收集了DDI解释数据，并开发了多种模型进行广泛实验与分析。我们的模型能够为已知药物间的未知DDI提供准确解释。本文为DDI预测领域贡献了新的工具，并为DDI预测解释生成研究奠定了坚实基础。