Computational Estimation of Potential Inhibitors from the Known Drugs against the Main Protease of SARS-CoV-2

The coronavirus disease (COVID-19) pandemic caused by a severe acute respiratory

syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread worldwide recently, leading to

a global social and economic disruption. Although the emergently approved vaccine

programs against SARS-CoV-2 have been rolled out globally, the number of COVID-19

daily cases and deaths has remained significantly high. Here, we attempted to

computationally screen for possible medications for COVID-19 via rapidly estimate the

highly potential inhibitors from an FDA-approved drug database against the main

protease (Mpro) of SARS-CoV-2. The approach combined molecular docking and fast

pulling of ligand (FPL) simulations that were demonstrated to be accurate and suitable

for quick prediction of SARS-CoV-2 Mpro inhibitors. The results suggested that twentyseven compounds were capable of strongly associating with SARS-CoV-2 Mpro. Among

them, the seven top leads are daclatasvir, teniposide, etoposide, levoleucovorin,

naldemedine, cabozantinib, and irinotecan. The potential application of these drugs in

COVID-19 therapy has thus been discussed.