  There have been more than 850,000 confirmed cases and over 48,000 deaths from
the human coronavirus disease 2019 (COVID-19) pandemic, caused by novel severe
acute respiratory syndrome coronavirus (SARS-CoV-2), in the United States
alone. However, there are currently no proven effective medications against
COVID-19. Drug repurposing offers a promising way 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, genes, pathways, and expressions, from a large
scientific corpus of 24 million PubMed publications. Using Amazon AWS computing
resources, we identified 41 repurposable drugs (including indomethacin,
toremifene and niclosamide) whose therapeutic association with COVID-19 were
validated by transcriptomic and proteomic data in SARS-CoV-2 infected human
cells and data from ongoing clinical trials. While 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 of accelerating therapeutic development for COVID-19.
