Abstract
The SARS-CoV-2 3-chymotrypsin-like (3CL(pro)) protease is a key target for the development of COVID-19 therapeutics. While ensitrelvir and nirmatrelvir are approved drugs for treatment, the continuous research and development for new antiviral drugs is necessary to combat the emergence of variants and other related viruses. This study employed structure- and ligand-based computer-assisted approaches to identify new 3CL(pro) nonpeptidomimetic inhibitors. Using data from COVID Moonshot, NCATS, and the literature, computational methods such as shape-based, ensemble docking, and machine learning (ML) techniques were developed, achieving robust validation metrics: AUC = 87%, EF = 7, BEDROC = 60% for shape-based; AUC = 87%, EF = 7.03, BEDROC = 62% for ensemble docking, and ACC = 81%, MCC = 62% for ML models, combing Random forest + ECFP4 fingerprint. These models were utilized in virtual screening (VS) campaigns using the H3D and ChemBridge libraries, from which six promising hits with IC(50) values ≤80 µM were identified, including LabMol-499 with an IC(50) of 13.71 µM and a K(i) of 21.74 µM. Moreover, we found that LabMol-499 acts as a noncompetitive, reversible inhibitor of 3CL(pro). These findings provide a foundation for hit-to-lead optimization of new nonpeptidomimetic 3CL(pro) inhibitors.