Lead Identification against 3C-like Protease of SARS-CoV-2 Via Target-based Virtual Screening and Molecular Dynamics Simulation

Background: The COVID-19 pandemic has prompted the human population’s emotional, social, and financial loss worldwide and presents an unprecedented challenge to health, food, and working styles. However, the exceptional implication of vaccinations at every nook and corner of the world has been breaking the rate of infection and mortality to a greater extent; in the case of potential oral drugs, we still need to decipher more palliative and therapeutic measures to develop effective antiviral drug candidates. Materials and Methods: The study exploits the structurebased virtual screening (SBVS) approach to identify small molecule inhibitors against 3C-like protease (3CLPRO) of SARS-CoV-2 from more than five million compounds of the MCULE database. Results: Four basic properties viz., molecular weight (≤ 500 g/m), hydrogen bond donor (≤ 5), hydrogen bond acceptor (≤ 10), and logP (≤ 5) as an initial filter were employed in SBVS workflow that extracted 2,235,82 compounds that were subsequently reduced to 22 ligands showing lesser ∆G values than reference drug nirmatrelvir (-7.9 kcal/mol). Upon toxicity check, 10 ligands were obtained that further curtailed to 9 molecules when passed through the BOILED-Egg model of the ADME. Upon compliance of druglikeness other than Lipinski viz., Ghose, Veber, Egan, Muegge, and bioavailability score 7 molecules were shortlisted in which 5 molecules exhibited zero PAINS and Brenk alert. Conclusion: At last, only 1 ligand hit (Mcule-3133395989) was identified that obeyed hydrogen bond selection criterion (ligand-3CLPRO complex ≥ 3 HBs). RMSD, RMSF, SASA, ΔGsolv, Rg, and HBs parameters of MD simulations predict Mcule-3133395989 more stable and promising antiviral agent compared to nirmatrelvir.