Login / Signup

Repositioning of 8565 Existing Drugs for COVID-19.

Kaifu GaoDuc Duy NguyenJiahui ChenRui WangGuo-Wei Wei
Published in: The journal of physical chemistry letters (2020)
The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected over 7.1 million people and led to over 0.4 million deaths. Currently, there is no specific anti-SARS-CoV-2 medication. New drug discovery typically takes more than 10 years. Drug repositioning becomes one of the most feasible approaches for combating COVID-19. This work curates the largest available experimental data set for SARS-CoV-2 or SARS-CoV 3CL (main) protease inhibitors. On the basis of this data set, we develop validated machine learning models with relatively low root-mean-square error to screen 1553 FDA-approved drugs as well as another 7012 investigational or off-market drugs in DrugBank. We found that many existing drugs might be potentially potent to SARS-CoV-2. The druggability of many potent SARS-CoV-2 3CL protease inhibitors is analyzed. This work offers a foundation for further experimental studies of COVID-19 drug repositioning.
Keyphrases
  • sars cov
  • respiratory syndrome coronavirus
  • coronavirus disease
  • machine learning
  • drug discovery
  • big data
  • drug induced
  • clinical trial
  • emergency department
  • high throughput
  • study protocol