Quantitative structure–activity relationship-based computational approaches

ABSTRACT

World Health Organization (WHO) categorized novel Coronavirus disease (COVID-19), triggered by severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2) as a world pandemic. This infection has been increasing alarmingly by instigating enormous social and economic disturbance. In order to retort rapidly, the inhibitors previously designed against different targets will be a good starting point for anti-SARS-CoV-2 inhibitors. The chapter deals with various quantitative structure–activity relationship (QSAR) techniques currently used in computational drug design and their applications and advantages in the overall drug design process. The chapter reviews current QSAR studies carried out against SARS-COV-2. The QSAR study design is composed of some major facets (1) classification QSAR-based data mining of various inhibitors, (2) QSAR-based virtual screening to recognize molecules that could be effective against assumed COVID-19 protein targets. (3) Finally validation of hits through receptor–ligand interaction analysis. This approach is used overall to help in the process of COVID-19 drug discovery. It presents key conceptions, sets the stage for QSAR-based screening of active molecules against SARS-COV-2. Moreover, the QSAR models reported can be further used to monitor huge databases. This chapter gives a first-hand review of all the current QSAR parameters developed for generating a good QSAR model against SARS-COV-2 and subsequently designing a drug against the COVID-19 virus. © 2022 Elsevier Inc. All rights reserved.