Drug Repurposing Therapeutics Prediction using Hierarchical Graph Neural Network

Abstract

Discovering new therapeutic indications for existing drugs is the essential part of drug repurposing. As a safe, low-cost and time-saving drug discovery technique, drug repurposing is attracting more and more research attention. However, there are still many potential drug-disease therapeutic effects uncovered in biological data sources. The advance of computation techniques offers great support for drug repurposing. In this study, we propose a computational approach, named BiGNN, to explore potential drug-disease associations. First, we collect multiple biological data sources to construct the heterogeneous information networks. Then BiGNN employs a bilevel graph-based neural network frameworks to aggregating features via node-level embedding and graph-level embedding respectively. Finally, the aggregated features are used to identify drug-disease associations. The experiment results demonstrate that BiGNN achieves outperforming performance with the average AUPROC of 0.95S and AUPR of 0.647 when evaluating on two benchmark datasets by 10-fold cross-validation.