ExDDI: Explaining Drug-Drug Interaction Predictions with Natural Language

TL;DR

This paper introduces ExDDI, a model that predicts drug-drug interactions and provides natural language explanations to reveal underlying mechanisms, enhancing trust and interpretability in DDI predictions.

Contribution

It presents a novel approach to generate natural language explanations for DDI predictions, integrating pharmacodynamics and pharmacokinetics insights.

Findings

Models accurately explain unknown DDIs

Collected DDI explanations from DDInter and DrugBank

Lays foundation for explainable DDI prediction

Abstract

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.