Repurpose Open Data to Discover Therapeutics for COVID 19 Using Deep Learning
There have been more than 2.2 million confirmed cases and over 120 000 deaths from the human coronavirus disease 2019 (COVID 19) pandemic, caused by the novel severe acute respiratory syndrome coronavirus (SARS CoV 2) in the United States alone. However, there is currently a lack of proven effective medications against COVID 19. Drug repurposing offers a promising route for the development of prevention and treatment strategies for COVID 19. This study reports an integrative, network based deep learning methodology to identify repurposable drugs for COVID 19 (termed CoV KGE) Specifically, we built a comprehensive knowledge graph that includes 15 million edges across 39 types of relationships connecting drugs, diseases, proteins/genes, pathways, and expression from a large scientific corpus of 24 million PubMed publications. Using Amazon's AWS computing resources and a network based, deep learning framework, we identified 41 repurposable drugs (including dexamethasone, indomethacin, niclosamide, and toremifene) whose therapeutic associations with COVID 19 were validated by transcriptomic and proteomics data in SARS CoV 2 infected human cells and data from ongoing clinical trials. Whereas this study by no means recommends specific drugs, it demonstrates a powerful deep learning methodology to prioritize existing drugs for further investigation, which holds the potential to accelerate therapeutic development for COVID 19.
Journal of proteome research