Artificial intelligence (AI)-based drug repurposing is an emerging strategy to identify drug candidates to treat rare diseases. However, cutting-edge algorithms based on deep learning typically do not provide a human understandable explanation supporting their predictions. This is a problem because it hampers biologists’ ability to decide which predictions are the most plausible drug candidates to test in costly lab experiments. In this study, we propose rd-explainer a novel AI drug repurposing method for rare diseases which obtains possible drug candidates together with human understandable explanations. The method is based on graph neural network technology and explanations were generated as semantic graphs using state-of-the-art explainable AI (XAI). The model learns features from current background knowledge on the target rare disease structured as a knowledge graph, which integrates curated facts and their evidence on different biomedical entities such as symptoms, drugs, genes, and ortholog genes. Our experiments demonstrate that our method has excellent performance that is superior to state-of-the-art models. We investigated the application of XAI on drug repurposing for rare diseases and we prove our method is capable of discovering plausible drug candidates based on testable explanations.
Perdomo-Quinteiro et al. (Wed,) studied this question.