  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
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 is a post-peer-review, pre-copyedit version of an
article published in Scientific Reports The final authenticated version is
available online at: https://www.nature.com/articles/s41598-021-02353-5
