In Search of Non-covalent Inhibitors of SARS–CoV–2 Main Protease: Computer Aided Drug Design Using Docking and Quantum Chemistry

Abstract

Two stages virtual screening of a database containing several thousand low molecular weight organic compounds is performed with the goal to find inhibitors of SARS–CoV–2 main protease. Overall near 41000 different 3D molecular structures have been generated from the initial molecules taking into account several conformers of most molecules. At the first stage the classical SOL docking program is used to determine most promising candidates to become inhibitors. SOL employs the MMFF94 force field, the genetic algorithm (GA) of the global energy optimization, takes into account the desolvation effect arising upon protein-ligand binding and the internal stress energy of the ligand. Parameters of GA are selected to perform the meticulous global optimization, and for docking of one ligand several hours on one computing core are needed on the average. The main protease model is constructed on the base of the protein structure from the Protein Data Bank complex 6W63. More than 1000 ligands structures have been selected for further postprocessing. The SOL score values of these ligands are more negative than the threshold of −6.3 kcal/mol obtained for the native X77 ligand docking. Subsequent calculation of the protein-ligand binding enthalpy by the PM7 quantum-chemical semiempirical method with COSMO solvent model have narrowed down the number of best candidates. Finally, the diverse set of 20 most perspective candidates for the in vitro validation are selected.