Journal of Computer Chemistry, Japan
Online ISSN : 1347-3824
Print ISSN : 1347-1767
ISSN-L : 1347-1767
Letters (Selected Paper)
Hybrid in Silico Drug Discovery Study toward the Development of Oral Antivirals for COVID-19
Uika KOSHIMIZUJunichi ONOYoshifum FUKUNISHIHiromi NAKAI
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2022 Volume 21 Issue 2 Pages 48-51

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Abstract

Hybrid in silico drug discovery was performed by combining large-scale quantum molecular dynamics (QMD) simulations with the conventional in silico drug discovery, focusing on developing covalent inhibitors against the main protease (Mpro) of SARS-CoV-2, the virus responsible for ongoing COVID-19 pandemic. The crystal structures and instantaneous structures obtained from the large-scale QMD simulations for Mpro were used as receptors in ensemble docking to estimate the binding affinities of the four ligands: the natural substrate recognized by Mpro, that recognized by the other enzyme of SARS-CoV-2, approved covalent inhibitor (PF-07321332), and the new candidate compound X determined from virtual screening. The present result shows that the binding affinity of X was comparable to that of PF-07321332, demonstrating the potency of our drug discovery.

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