In continuation of our previous effort, different in silico selection methods were applied to 310 naturally isolated metabolites that exhibited antiviral potentialities before. The applied selection methods aimed to pick the most relevant inhibitor of SARS-CoV-2 nsp10. At first, a structural similarity study against the co-crystallized ligand, S-Adenosyl Methionine (SAM), of SARS-CoV-2 nonstructural protein (nsp10) (PDB ID: 6W4H) was carried out. The similarity analysis culled 30 candidates. Secondly, a fingerprint study against SAM preferred compounds 44, 48, 85, 102, 105, 182, 220, 221, 282, 284, 285, 301, and 302. The docking studies picked 48, 182, 220, 221, and 284. While the ADMET analysis expected the likeness of the five candidates to be drugs, the toxicity study preferred compounds 48 and 182. Finally, a density-functional theory (DFT) study suggested vidarabine (182) to be the most relevant SARS-Cov-2 nsp10 inhibitor.
Keywords: ADMET; COVID-19; DFT; SARS-Cov-2 nsp10; fingerprint; molecular docking; natural products; structural similarity; toxicity.