Virtual Screening of Heterocyclic Molecules to Identify Potential SARS-COV2 virus Mpro Protease Inhibitors for Further Medicinal Chemistry design

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Abstract In this work, we aimed to identify potential SARS-COV2 virus Mpro protease inhibitors using molecular docking. As a validation step of our docking approach, we used 7 known Mpro inhibitors and predicted their binding energies. We found a very good correlation (R = -0.86) between the binding energies and the pIC50 (-LogIC50) values. We then undertook virtual screening of 50753 heterocyclic molecules from the PubChem Database. The screened molecules were first filtered out using Lipinski, Veber and Ghose rules resulting in 21 142 which was submitted to the docking phase. The docked compounds were ranked according to their predicted binding energy to the protease and a threshold of -9.0 Kcal/mol was applied resulting in a set of 2711 hits. These hits were split into different groups according to their chemical class using a rule-based classification approach. The best compound from each of the most populated classes were subjected to binding mode analysis which led to ligand-receptor interaction maps and suggested some medicinal chemistry-oriented modifications to further optimize the potency of the obtained hits. Predicted IC50 of the hit molecules ranged from 0.85 to 0.43 µM against SARS-COV2 virus Mpro protease.

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