  Currently, the number of patients with COVID-19 has significantly increased.
Thus, there is an urgent need for developing treatments for COVID-19. Drug
repurposing, which is the process of reusing already-approved drugs for new
medical conditions, can be a good way to solve this problem quickly and
broadly. Many clinical trials for COVID-19 patients using treatments for other
diseases have already been in place or will be performed at clinical sites in
the near future. Additionally, patients with comorbidities such as diabetes
mellitus, obesity, liver cirrhosis, kidney diseases, hypertension, and asthma
are at higher risk for severe illness from COVID-19. Thus, the relationship of
comorbidity disease with COVID-19 may help to find repurposable drugs. To
reduce trial and error in finding treatments for COVID-19, we propose building
a network-based drug repurposing framework to prioritize repurposable drugs.
First, we utilized knowledge of COVID-19 to construct a disease-gene-drug
network (DGDr-Net) representing a COVID-19-centric interactome with components
for diseases, genes, and drugs. DGDr-Net consisted of 592 diseases, 26,681
human genes and 2,173 drugs, and medical information for 18 common
comorbidities. The DGDr-Net recommended candidate repurposable drugs for
COVID-19 through network reinforcement driven scoring algorithms. The scoring
algorithms determined the priority of recommendations by utilizing graph-based
semi-supervised learning. From the predicted scores, we recommended 30 drugs,
including dexamethasone, resveratrol, methotrexate, indomethacin, quercetin,
etc., as repurposable drugs for COVID-19, and the results were verified with
drugs that have been under clinical trials. The list of drugs via a data-driven
computational approach could help reduce trial-and-error in finding treatment
for COVID-19.
