Computational Approaches for Lead Discovery against SARS-CoV-2 3C-Like Protease: Virtual Screening and Molecular Dynamics Studies

Abstract

Background: The COVID-19 pandemic has caused significant difficulties in multiple emotional, social, and financial areas. Despite the positive effects of vaccination in reducing infection and fatality rates, the need for efficient antiviral medications, particularly those that can be taken orally, remains a critical concern. Methods: A virtual screening method based on structure, referred to as SBVS, was used to identify potential inhibitory small molecules that specifically target the 3C-like protease (3CLPRO) found in SARS-CoV-2. Results: The filtering process for potential ligands involved strict criteria based on their molecular properties, including a molecular weight limit of 500 g/mol, maximum of five hydrogen bond donors, maximum of 10 hydrogen bond acceptors, and logP limit of 5. This was done to identify five candidates with lower ∆G values than the reference drugs lopinavir (-8.19 kcal/mol) and ritonavir (-8.04 kcal/mol). Three hits were identified through further evaluation using the hydrogen bond criteria and the BOILED-Egg model. The pharmacokinetic attributes of these two hits were compared with those of the reference drugs lopinavir and ritonavir. Conclusion: The molecular dynamics simulation (20 ns) outcomes unequivocally demonstrated the stability and promising nature of MCULE-2367618737 as a possible lead compound against the targeted 3CLPRO.