Drug Repurposing Opportunities in Shapley Space

Repurposing is the process of finding new indications for already available drugs. Mathematical and computational modeling facilitates the complex and time-consuming process of identifying new uses for known compounds. Motivated by the study of drug repurposing, we present an unsupervised node embedding algorithm that learns latent representations of drugs and diseases based on adaptive sampling of nodes' nearest neighbors. The learned latent representations were then fed to train a model in order to predict the existence of possible links between pairs of nodes. We studied the proximities in the model's decision space to identify hidden similarities among drugs and diseases and managed to find dozens of drug repurposing candidates.