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Interactive Visual Explanations for Deep Drug Repurposing
Qianwen Wang · Payal Chandak · Marinka Zitnik

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in Interpretable Machine Learning in Healthcare »

Faced with skyrocketing costs for developing new drugs from scratch, repurposing existing drugs for new uses is an enticing alternative that considerably 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.

Author Information

Qianwen Wang (Harvard University)
Payal Chandak (Harvard-MIT Health Sciences and Technology)
Marinka Zitnik (Harvard University)

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