Hybrid in Silico Drug Discovery Study toward the Development of Oral Antivirals for COVID-19

IF 0.1 Q4 CHEMISTRY, MULTIDISCIPLINARY
Uika Koshimizu, Junichi Ono, Y. Fukunishi, Hiromi Nakai
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引用次数: 0

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 (M-pro) 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 M-pro were used as receptors in ensemble docking to estimate the binding affinities of the four ligands: the natural substrate recognized by M-pro, 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.
面向新型冠状病毒口服抗病毒药物开发的硅杂化药物发现研究
通过将大规模量子分子动力学(QMD)模拟与传统的计算机药物发现相结合,进行了混合计算机药物发现,重点是开发针对SARS-CoV-2主要蛋白酶(M-pro)的共价抑制剂。SARS-CoV-2是导致当前COVID-19大流行的病毒。利用大规模QMD模拟获得的M-pro晶体结构和瞬时结构作为受体进行集合对接,估计M-pro识别的天然底物、SARS-CoV-2的另一酶识别的底物、批准的共价抑制剂(PF-07321332)和虚拟筛选确定的新候选化合物X的结合亲和力。目前的结果表明,X的结合亲和力与PF-07321332相当,证明了我们的药物发现的效力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Computer Chemistry-Japan
Journal of Computer Chemistry-Japan CHEMISTRY, MULTIDISCIPLINARY-
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