A combination of structure-based virtual screening and experimental strategies to identify the potency of caffeic acid ester derivatives as SARS-CoV-2 3CL inhibitor from an in-house database.

Drug development requires significant time and resources, and computer-aided drug discovery techniques that integrate chemical and biological spaces offer valuable tools for the process. This study focused on the field of COVID-19 therapeutics and aimed to identify new active non-covalent inhibitors for 3CL, a key protein target. By combining in silico and in vitro approaches, an in-house database was utilized to identify potential inhibitors. The drug-likeness criteria were considered to pre-filter 553 compounds from 12 groups of natural products. Using structure-based virtual screening, 296 compounds were identified that matched the chemical features of SARS-CoV-2 3CL peptidomimetic inhibitor pharmacophore models. Subsequent molecular docking resulted in 43 hits with high binding affinities. Among the hits, caffeic acid analogs showed significant interactions with the 3CL active site, indicating their potential as promising candidates. To further evaluate their efficacy, enzyme-based assays were conducted, revealing that two ester derivatives of caffeic acid (4k and 4l) exhibited more than a 30% reduction in 3CL activity. Overall, these findings suggest that the screening approach employed in this study holds promise for the discovery of novel anti-SARS-CoV-2 therapeutics. Furthermore, the methodology could be extended for optimization or retrospective evaluation to enhance molecular targeting and antiviral efficacy of potential drug candidates.