Computational discovery of small drug-like compounds as potential inhibitors of SARS-CoV-2 main protease

A computational approach to in silico drug discovery was carried out to identify small drug-like compounds able to show structural and functional mimicry of the high affinity ligand X77, potent non-covalent inhibitor of SARS-COV-2 main protease (M^Pro). In doing so, the X77-mimetic candidates were predicted based on the crystal X77-M^Pro structure by a public web-oriented virtual screening platform Pharmit. Models of these candidates bound to SARS-COV-2 M^Pro were generated by molecular docking, quantum chemical calculations and molecular dynamics simulations. At the final point, analysis of the interaction modes of the identified compounds with M^Pro and prediction of their binding affinity were carried out. Calculation revealed 5 top-ranking compounds that exhibited a high affinity to the active site of SARS-CoV-2 M^Pro. Insights into the ligand - M^Pro models indicate that all identified compounds may effectively block the binding pocket of SARS-CoV-2 M^Pro, in line with the low values ​​of binding free energy and dissociation constant. Mechanism of binding of these compounds to M^Pro is mainly provided by van der Waals interactions with the functionally important residues of the Enzyme, such as His-41, Met-49, Cys-145, Met-165, and Gln-189 that play a role of the binding hot spots assisting the predicted molecules to effectively interact with the M^Pro active site. The data obtained show that the identified X77-mimetic candidates may serve as good scaffolds for the design of novel Antiviral agents able to target the active site of SARS-CoV-2 M^Pro.Communicated by Ramaswamy H. Sarma.