Identification of Potential Ligands of the Main Protease of Coronavirus SARS-CoV-2 (2019-nCoV) Using Multimodal Generative Neural-Networks

IF 0.4 Q4 CHEMISTRY, ANALYTICAL
S. R. Zia
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引用次数: 0

Abstract

The recent outbreak of coronavirus disease 2019 (COVID-19) is posing a global threat to human population. The pandemic caused by novel coronavirus (2019-nCoV), also called as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2); first emerged in Wuhan city, Hubei province of China in December 2019. The rapid human to human transmission has caused the contagion to spread world-wide affecting 244,385,444 (244.4 million) people globally causing 4,961,489 (5 million) fatalities dated by 27 October 2021. At present, 6,697,607,393 (6.7 billion) vaccine doses have been administered dated by 27 October 2021, for the prevention of COVID-19 infections. Even so, this critical and threatening situation of pandemic and due to various variants’ emergence, the pandemic control has become challenging; this calls for gigantic efforts to find new potent drug candidates and effective therapeutic approaches against the virulent respiratory disease of COVID-19. In the respiratory morbidities of COVID-19, the functionally crucial drug target for the antiviral treatment could be the main protease/3-chymotrypsin protease (Mpro/3CLpro) enzyme that is primarily involved in viral maturation and replication. In view of this, in the current study I have designed a library of small molecules against the main protease (Mpro) of coronavirus SARS-CoV-2 (2019-nCoV) by using multimodal generative neural-networks. The scaffold-based molecular docking of the series of compounds at the active site of the protein was performed; binding poses of the molecules were evaluated and protein-ligand interaction studies followed by the binding affinity calculations validated the findings. I have identified a number of small promising lead compounds that could serve as potential inhibitors of the main protease (Mpro) enzyme of coronavirus SARS-CoV-2 (2019-nCoV). This study would serve as a step forward in the development of effective antiviral therapeutic agents against the COVID-19.
利用多模态生成神经网络识别冠状病毒SARS-CoV-2 (2019-nCoV)主要蛋白酶的潜在配体
最近爆发的2019冠状病毒病(COVID-19)对人类构成了全球性威胁。由新型冠状病毒(2019-nCoV)引起的大流行,也被称为严重急性呼吸综合征冠状病毒-2 (SARS-CoV-2);2019年12月首次在中国湖北省武汉市出现。人与人之间的快速传播导致传染病在全球范围内蔓延,截至2021年10月27日,全球有244,385,444人(2.444亿人)受到影响,造成4,961,489人(500万人)死亡。目前,为预防COVID-19感染,截至2021年10月27日已接种了6,697,607,393剂(67亿剂)疫苗。即便如此,这种严重和威胁的大流行形势以及各种变体的出现,使大流行的控制变得具有挑战性;这就要求我们付出巨大努力,寻找新的强效候选药物和有效的治疗方法,以对抗COVID-19这一致命呼吸道疾病。在COVID-19呼吸道疾病中,抗病毒治疗的功能关键药物靶点可能是主要参与病毒成熟和复制的主要蛋白酶/3-凝乳胰蛋白酶(Mpro/3CLpro)酶。鉴于此,在本次研究中,我利用多模态生成神经网络设计了一个针对冠状病毒SARS-CoV-2 (2019-nCoV)主要蛋白酶(Mpro)的小分子库。在蛋白活性位点对一系列化合物进行了基于支架的分子对接;评估了分子的结合姿态,并进行了蛋白质与配体的相互作用研究,随后进行了结合亲和力计算,验证了这些发现。我已经确定了一些有希望的小先导化合物,它们可以作为冠状病毒SARS-CoV-2 (2019-nCoV)主要蛋白酶(Mpro)酶的潜在抑制剂。这项研究将成为开发针对COVID-19的有效抗病毒治疗剂的一步。
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
French-Ukrainian Journal of Chemistry
French-Ukrainian Journal of Chemistry CHEMISTRY, ANALYTICAL-
自引率
0.00%
发文量
13
审稿时长
4 weeks
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