Computer-aided drug design of novel nirmatrelvir analogs inhibiting main protease of Coronavirus SARS-CoV-2

Abstract

A computer-aided drug design of new derivatives of nirmatrelvir, an orally active inhibitor of the main-protease (Mpro) of the severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2), was performed to identify its analogues with a higher antiviral potency. The following workflow was used: first, an evolutionary library composed of 1,866 analogues was generated starting from a parent nirmatrelvir scaffold and going through small mutation, fitness scoring, ranking, and selection. Second, the generated library was preprocessed and filtered against a 3-D pharmacophore model of nirmatrelvir built from its X-ray structure in a co-crystalized complex with the Mpro enzyme, allowing us to reduce the chemical space to 32 active analogues. Third, structure-based molecular docking against two different enzyme structures further ranked these active candidates, so that up to eight better-binding analogs were identified. The selected hit-analogues target the Mpro enzymes of SARS-CoV-2 with a higher binding affinity than a parent nirmatrelvir. The main structural modifications that increase the overall inhibitory affinity are identified at the azabicyclo[3.1.0] hexane and 2-oxopyrrolidine fragments. A characteristic structural feature of the inhibitor binding with the Mpro active centre is the similar location of the trifluoroacetylamino fragment, which is observed for most hit-analogues. The suggested workflow of the computer-aided rational design of new antiviral noncovalent inhibitors based on the scaffold of approved drugs is a promising, extremely low-cost, and time-efficient approach for the development of new potential pharmaceutical ingredients for the treatment of Coronavirus Disease 2019.