The transmission and infectivity of COVID-19 have caused a pandemic that has lasted for several years. This is due to the constantly changing variants and subvariants that have evolved rapidly from SARS-CoV-2. To discover drugs with therapeutic potential for COVID-19, we focused on the 3CL protease (3CLpro) of SARS-CoV-2, which has been proven to be an important target for COVID-19 infection. Computational prediction techniques are quick and accurate enough to facilitate the discovery of drugs against the 3CLpro of SARS-CoV-2. In this paper, we used both ligand-based virtual screening and structure-based virtual screening to screen the traditional Chinese medicine small molecules that have the potential to target the 3CLpro of SARS-CoV-2. MD simulations were used to confirm these results for future in vitro testing. MCCS was then used to calculate the normalized free energy of each ligand and the residue energy contribution. As a result, we found ZINC15676170, ZINC09033700, and ZINC12530139 to be the most promising antiviral therapies against the 3CLpro of SARS-CoV-2.
Keywords: 3CL protease; SARS-CoV-2; molecular dynamics simulations; traditional Chinese medicine; virtual screening.