ExDDI: Explaining Drug-Drug Interaction Predictions with Natural Language
This work addresses the need for explanatory insights in DDI prediction to improve medication safety, representing an incremental advancement by adding explanation generation to existing prediction methods.
The paper tackles the problem of predicting unknown drug-drug interactions (DDIs) by generating natural language explanations to enhance trust, with models providing accurate explanations for unknown DDIs between known drugs.
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.