Mass Enhanced Node Embeddings for Drug Repurposing

Graph representation learning has recently emerged as a promising approach to solve pharmacological tasks by modeling biological networks. Among the different tasks, drug repurposing, the task of identifying new uses for approved or investigational drugs, has attracted a lot of attention recently. In this work, we propose a node embedding algorithm for the problem of drug repurposing. The proposed algorithm learns node representations that capture the influence of nodes in the biological network by learning a mass term for each node along with its embedding. We apply the proposed algorithm to a multiscale interactome network and embed its nodes (i. e., proteins, drugs, diseases and biological functions) into a low-dimensional space. We evaluate the generated embeddings in the drug repurposing task. Our experiments show that the proposed approach outperforms the baselines and offers an improvement of 53.33% in average precision over typical walk-based embedding approaches.