# A case-based explainable graph neural network framework for mechanistic drug repositioning

## TL;DR

This paper introduces DBR-X, an explainable AI framework for drug repositioning that provides interpretable predictions of drug-disease associations.

## Contribution

DBR-X is a novel explainable GNN framework that combines link prediction with mechanistic explanations for drug repositioning.

## Key findings

- DBR-X outperforms existing GNN models in identifying known drug-disease associations.
- The model generates faithful and stable explanations validated through deletion and insertion studies.
- DBR-X provides multi-hop mechanistic explanations that align with manually curated drug mechanisms.

## Abstract

Drug repositioning offers a cost-effective alternative to traditional drug development by identifying new uses for existing drugs. Recent advances leverage Graph Neural Networks (GNNs) to model complex biological data, showing promise in predicting novel drug-disease associations; however, these frameworks often lack explainability, a critical factor for validating predictions and understanding drug mechanisms. Here, we introduce Drug-Based Reasoning Explainer (DBR-X), an explainable GNN model that integrates a link-prediction module with a path-identification module to generate interpretable and faithful explanations. When benchmarked against other GNN-based link-prediction frameworks, DBR-X achieves superior performance in identifying known drug-disease associations, demonstrating higher accuracy across all evaluation metrics. The quality of DBR-X biological explanations was evaluated through multiple complementary approaches, including comparison with manually curated drug mechanisms, assessment of explanation faithfulness using deletion and insertion studies, and measurement of stability under graph perturbations. Together, these results show that DBR-X advances the state of the art in drug repositioning while providing multi-hop mechanistic explanations that can facilitate the translation of computational predictions into clinical applications.

Availability and implementation: DBR-X package is freely accessible from online repository https://github.com/SuLab/DBR-X