DDTExplainer: Mining Drug-Disease Therapeutic Mechanisms based on GNN Explainability

For a clinical prescription, clarifying the molecular mechanisms of actions (MMOAs) of the drug-disease interaction is helpful to optimize treatment, suggest possible side effects, and realize individualized treatment. Considering the relations among multiple biomedical entities, such as drugs, diseases, targets (genes), and pathways, what paths can be extracted connecting these biomedical entities to resemble the real mechanisms of a specific drug to a particular disease? Answering this question is crucial for understanding the underlying molecular mechanisms behind complex drug actions and identifying key pathways that can facilitate effective therapeutic interventions. In this paper, we propose an approach DDTExplainer that constructs a path-based graph neural network (GNN) explainer to mine the drug-disease therapeutic mechanisms. Technically, DDTExplainer transforms the drug-disease therapeutic mechanisms mining task into a GNN-based link prediction model explanation task. Firstly, a GNN-based drug-disease therapeutic prediction model is trained and joint-optimized with a translation-based graph embedding model. Secondly, mask learning is utilized to find the most prediction-influential edges and generate the path-based explanations with the shortest path algorithm. Finally, we assess the efficacy of DDTExplainer on a ground-truth dataset that consists of labeled entries describing the drug-disease therapeutic mechanisms. These labels are derived from well-established drug-target interactions, disease-target interactions, as well as target-pathway relationships, which were verified by wet experiments.