Computer-aided drug design for virtual-screening and active-predicting of main protease (Mpro) inhibitors against SARS-CoV-2
Computer-aided drug design for virtual-screening and active-predicting of main protease (Mpro) inhibitors against SARS-CoV-2
R. Dai,Hongwei Gao,Ruiling Su
TLDR
These results provided a theoretical foundation for future studies of Mpro inhibitors against SARS-CoV-2 and showed that ENA482732 was the best compound.
Abstract
Introduction: SARS-CoV-2 is a novel coronavirus with highly contagious and has posed a significant threat to global public health. The main protease (Mpro) is a promising target for antiviral drugs against SARS-CoV-2. Methods: In this study, we have used pharmacophore-based drug design technology to identify potential compounds from drug databases as Mpro inhibitors. Results: The procedure involves pharmacophore modeling, validation, and pharmacophore-based virtual screening, which identifies 257 compounds with promising inhibitory activity. Discussion: Molecular docking and non-bonding interactions between the targeted protein Mpro and compounds showed that ENA482732 was the best compound. These results provided a theoretical foundation for future studies of Mpro inhibitors against SARS-CoV-2.