期刊
JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS
卷 39, 期 13, 页码 4764-4773出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/07391102.2020.1780946
关键词
COVID-19; SARS-CoV-2; SARS-CoV PLpro; Monte Carlo based optimization; QSAR based virtual screening; ADME; molecular docking
资金
- Council of Scientific and Industrial Research (CSIR), New Delhi, India [09/096(0967)/2019-EMR-I]
- RUSA 2.0 of UGC, New Delhi, India
The World Health Organization declared COVID-19 as a global pandemic requiring rapid response; designing inhibitors against SARS-CoV-2 as a starting point for anti-viral drugs; the study integrated different drug design strategies, useful for COVID-19 drug discovery.
World Health Organization characterized novel coronavirus disease (COVID-19), caused by severe acute respiratory syndrome (SARS) coronavirus-2 (SARS-CoV-2) as world pandemic. This infection has been spreading alarmingly by causing huge social and economic disruption. In order to response quickly, the inhibitors already designed against different targets of previous human coronavirus infections will be a great starting point for anti-SARS-CoV-2 inhibitors. In this study, our approach integrates different ligand based drug design strategies of somein-housechemicals. The study design was composed of some major aspects: (a) classification QSAR based data mining of diverse SARS-CoV papain-like protease (PLpro) inhibitors, (b) QSAR based virtual screening (VS) to identifyin-housemolecules that could be effective against putative target SARS-CoV PLpro and (c) finally validation of hits through receptor-ligand interaction analysis. This approach could be used to aid in the process of COVID-19 drug discovery. It will introduce key concepts, set the stage for QSAR based screening of active molecules against putative SARS-CoV-2 PLpro enzyme. Moreover, the QSAR models reported here would be of further use to screen large database. This study will assume that the reader is approaching the field of QSAR and molecular docking based drug discovery against SARS-CoV-2 PLpro with little prior knowledge. Communicated by Ramaswamy H. Sarma
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