Computational drug repurposing: An approach to identify Inhibitors of the SARS‐CoV‐2 main protease

The COVID‐19 pandemic has significantly affected the global population and economy. Most recently, transmission rates have increased at an alarming rate as a second wave of infection is devastating many countries. While vaccines were recently approved by the FDA, there is utility for the development of small molecule therapeutics that can reduce symptoms and decrease hospitalization stays. Target specific, small‐molecule drugs with optimal safety profiles and physiochemical properties can be identified as shelf‐stable treatments for those already infected with coronavirus. Drug repurposing is a common strategy used to examine currently available drugs for new indications. The method may incorporate computational screening of large, virtual compound libraries with validation through experimental assays and structural studies. The objective of our study was to assemble an FDA approved drug‐screening library of drugs that may be explored for repurposing against the coronavirus targeting the SARS‐CoV‐2 main protease. We used a combination of cheminformatics, virtual screening, and secondary library optimization to identify potential drug hits. Drugs predicted to have affinity for the SARS‐CoV‐2 main protease based on docking scores include alosetron, remdesivir, amlodipine, and amoxicillin. We also confirmed that derivatives of our top hits also retain affinity for the protease. Furthermore, we conducted molecular dynamics (MD) simulations for these predicted drug hits to determine binding profiles with the protease. Results from the MD simulation reveal that our predicted hits make common contacts with similar residues including Gly‐143, Ser‐144, Glu‐166, and Gln‐189. While our future work will test the affinity of these drugs for the SARS‐CoV‐2 main protease, we believe our screening methods and simulations can provide ideas for target‐specific drug design.