Graph Representation Learning for Covid-19 Drug Repurposing

The development of entirely new drugs for emerging diseases is challenging, time consuming and very costly. This underscores the importance of drug repurposing (DR), where treatments are found among clinically approved drugs for other diseases. DR gained more popularity with the emergence of the SARS-COV-2 virus. The current pandemic almost halted normal life in most parts of the world. Despite global research efforts on this task, no effective treatment has yet been developed. This research study is an effort to leverage deep learning techniques in network medicine to screen potential therapeutic drugs for COVID-19. We treat drug repurposing as a link prediction task on a large heterogeneous high-quality knowledge graph containing interactions between medical entities like drugs, diseases and genes from various resources. Using Graph Neural Network (GNN) based on GraphSAGE model, we were able to achieve AUC of 0.97. We highlighted the top 5 new drugs for repurposing for COVID-19 including Flurithromycin, Radotinib and Memantine, and predicted 7 drugs currently in clinical trials ranked among the top 100 drugs. The experimental results confirm that the model can effectively predict the drug-disease interaction for COVID-19.