Amid the pandemic of 2019 novel coronavirus disease (COVID-19) infected by SARS-CoV-2, a
vast amount of drug research for prevention and treatment has been quickly conducted, but these
efforts have been unsuccessful thus far. Our objective is to prioritize repurposable drugs using a
drug repurposing pipeline that systematically integrates multiple SARS-CoV-2 and drug
interactions, deep graph neural networks, and in-vitro/population-based validations. We first
collected all the available drugs (n= 3,635) involved in COVID-19 patient treatment through
CTDbase. We built a SARS-CoV-2 knowledge graph based on the interactions among virus baits,
host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to
derive the candidate drug’s representation based on the biological interactions. We prioritized the
candidate drugs using clinical trial history, and then validated them with their genetic profiles, in
vitro experimental efficacy, and electronic health records. We highlight the top 22 drugs including
Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug
combinations that may synergistically target COVID-19. In summary, we demonstrated that the
integration of extensive interactions, deep neural networks, and rigorous validation can facilitate
the rapid identification of candidate drugs for COVID-19 treatment.
This paper had been uploaded to arXiv : https://arxiv.org/abs/2009.10931