Interactive Visual Explanations for Deep Drug Repurposing

Faced with skyrocketing costs for developing new drugs from scratch, repurposing existing drugs for new uses is an enticing alternative that con-siderably reduces safety risks and development costs. However, successful drug repurposing has been mainly based on serendipitous discoveries. Here, we present a tool that combines a graph transformer network with interactive visual explanations to assist scientists in generating, exploring, and understanding drug repurposing predictions. Leveraging semantic attention in our graph transformer network, our tool introduces a novel way to visualize meta path explanations that provide biomedical context for interpretation. Our results show that the tool generates accurate drug predictions and provides interpretable predictions.