The efficiency of high-throughput screening (HTS) and in-silico data analysis during medical emergencies: Identification of effective antiviral 3CLpro inhibitors

Abstract

The COVID-19 pandemic highlighted the importance of accelerating the drug discovery process. The 3-chymotrypsin-like protease (3CLpro) is a critical enzyme in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral replication process and was quickly identified as a prime target for drug development. This study leverages High-Throughput Screening (HTS) to identify novel 3CLpro inhibitors. We screened a different library of 325,000 compounds, leading to the discovery of two new chemical scaffolds with selective inhibitory activity against 3CLpro. In-silico analysis and further experimental validation, elucidated the binding modes and mechanisms of action, revealing a covalent inhibitor targeting the catalytic pocket and two allosteric inhibitors affecting the monomer/dimer equilibrium of 3CLpro. The identified compounds demonstrated significant antiviral activity in vitro, reducing SARS-CoV-2 replication in VeroE6 and Calu-3 cell lines. This study highlights the potential of combining HTS and computational approaches to accelerate the discovery of effective antiviral agents, suggesting a workflow to support the research and the design of effective therapeutic strategies.