Journal of Computer Chemistry, Japan
Online ISSN : 1347-3824
Print ISSN : 1347-1767
ISSN-L : 1347-1767
速報論文 (SCCJ Annual Meeting 2022 Spring Poster Award Article)
速報論文 (SCCJ Annual Meeting 2022 Spring Poster Award Article)
  • 小清水 初花, 小野 純一, 福西 快文, 中井 浩巳
    2022 年 21 巻 2 号 p. 48-51
    発行日: 2022年
    公開日: 2022/11/23
    ジャーナル フリー HTML

    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.