Ligand-based quantitative structural assessments of SARS-CoV-2 3CLpro inhibitors: An analysis in light of structure-based multi-molecular modeling evidences

被引:13
作者
Adhikari, Nilanjan [1 ]
Banerjee, Suvankar [1 ]
Baidya, Sandip Kumar [1 ]
Ghosh, Balaram [2 ]
Jha, Tarun [1 ]
机构
[1] Jadavpur Univ, Dept Pharmaceut Technol, Div Med & Pharmaceut Chem, Nat Sci Lab, Kolkata 700032, India
[2] Birla Inst Technol & Sci, Epigenet Res Lab, Pilani Hyderabad Campus, Hyderabad 500078, India
关键词
COVID-19; SARS-CoV-2 3CL(pro); QSAR; ANN; SVM; HQSAR; MOLECULAR-DYNAMICS SIMULATION; MAIN PROTEASE; PREDICTION; COVID-19; VIRUS;
D O I
10.1016/j.molstruc.2021.132041
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Due to COVID-19, the whole world is undergoing a devastating situation, but treatment with no such drug candidates still has been established exclusively. In that context, 69 diverse chemicals with potential SARS-CoV-2 3CL(pro) inhibitory property were taken into consideration for building different internally and externally validated linear (SW-MLR and GA-MLR), non-linear (ANN and SVM) QSAR, and HQSAR models to identify important structural and physicochemical characters required for SARS-CoV-2 3CL(pro) inhibition. Importantly, 2-oxopyrrolidinyl methyl and benzylester functions, and methylene (hydroxy) sulphonic acid warhead group, were crucial for retaining higher SARS-CoV-2 3CL(pro) inhibition. These GA-MLR and HQSAR models were also applied to predict some already repurposed drugs. As per the GA-MLR model, curcumin, ribavirin, saquinavir, sepimostat, and remdesivir were found to be the potent ones, whereas according to the HQSAR model, lurasidone, saquinavir, lopinavir, elbasvir, and paritaprevir were the highly effective SARS-CoV-2 3CL(pro) inhibitors. The binding modes of those repurposed drugs were also justified by the molecular docking, molecular dynamics (MD) simulation, and binding energy calculations conducted by several groups of researchers. This current work, therefore, may be able to find out important structural parameters to accelerate the COVID-19 drug discovery processes in the future. (C) 2021 Elsevier B.V. All rights reserved.
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页数:17
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