Chemical-informatics approach to COVID-19 drug discovery: Monte Carlo based QSAR, virtual screening and molecular docking study of some <i>in-house</i> molecules as papain-like protease (PLpro) inhibitors

TLDR

COVID‑19, caused by SARS‑CoV‑2, is a global pandemic that has spread rapidly, causing significant social and economic disruption, and existing inhibitors from prior coronavirus infections offer a promising starting point for anti‑SARS‑CoV‑2 drug development. This study aims to integrate ligand‑based drug design strategies to identify and validate in‑house molecules as potential inhibitors of SARS‑CoV‑2 papain‑like protease (PLpro). The authors performed classification QSAR data mining of diverse PLpro inhibitors, used QSAR‑based virtual screening to pinpoint promising in‑house compounds, and validated hits through receptor‑ligand interaction analysis. The developed QSAR models and virtual screening workflow can aid COVID‑19 drug discovery and serve as a tool for screening large compound databases. The study is communicated by Ramaswamy H.

Abstract

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 some in-house chemicals. 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 identify in-house molecules 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.