Computational approaches for identifying bioactive compounds inhibiting SARS-CoV-2 main protease

Abstract

The infectious disease caused by the novel coronavirus 2019 has devastatingly affected the global economy and society. However, the drug discovery process from concept to approval requires a significant investment of time and resources. To address these challenges, we employed structure-based virtual screening techniques, including drug-likeness screening, pharmacophore-based virtual screening, molecular docking, molecular dynamics simulation, and fragment molecular orbital calculation. The protein target of this investigation was the main protease or 3-chymotrypsin-like-protease (3CLpro) of the coronavirus, given its pivotal role in the viral replication process. Using our in-house database of natural products and their derivatives, we aimed to identify potent compounds with the potential for further development as anti-SARS-CoV-2 medications. Notably, the derivatives of sulfonamide chalcone (SWC422, SWC423, and SWC424) and ester derivatives of caffeic acid (4k and 4l) exhibited exceptional binding energy and substantial interactions with the 3CLpro binding pocket compared to peptidomimetic inhibitors (11a, 13b, and N3) and an FDA-approved drug (nirmatrelvir). While our findings show that in-silico strategies have the potential to identify new potent compounds that inhibit the 3CLpro activity of coronavirus, further studies such as enzyme inhibition assay and cell-based assay are necessary to ensure their effectiveness from these virtual screenings.