De novo Design of SARS-CoV-2 Main Protease Inhibitors

Abstract

The COVID-19 pandemic prompted many scientists to investigate remedies against SARS-CoV-2 and related viruses that are likely to appear in the future. As the main protease of the virus, MPro, is highly conserved among coronaviruses it has emerged as a prime target for developing inhibitors. Using a combination of virtual screening and molecular modeling, we identified small molecules that were easily accessible and could be quickly diversified. Biochemical assays confirmed a class of pyridones as low micromolar non-covalent inhibitors of the viral main protease.

On the origins of SARS-CoV-2 main protease inhibitors

“…6). 160 A repurposed curiosity would be the veterinary anticancer drug masitinib (37), it is a tyrosine kinase inhibitor which has also been patented for its modest effect on the replication of SARS-CoV-2. 161 Further research actually led to its co-crystallization with SARS-CoV-2-M pro (PDB 7JU7).…”

Identification of SARS-CoV-2 Mpro inhibitors through deep reinforcement learning for de novo drug design and computational chemistry approaches

“…[9] Another similar approach employing Reinforcement Learning has been applied retrospectively to demonstrate that a DGM could generate active molecules where these molecules were not included in the training dataset. [10] Finally, de novo design approaches, without reinforcement learning were applied to discover new SARS-COV-2 Mpro inhibitors for which two approaches were experimentally validated [11,12] while two other were not. [13,14]…”

Identification of SARS-CoV-2 Mpro inhibitors through deep reinforcement learning for de novo drug design and computational chemistry approaches

“…14 Another similar approach employing reinforcement learning has been applied retrospectively to demonstrate that a DGM could generate active molecules where these molecules were not included in the training dataset. 15 Finally, de novo design approaches, without reinforcement learning, were applied to discover new SARS-COV-2 Mpro inhibitors for which two approaches were experimentally validated 16,17 while two others were not. 18,19 In this study, we focused on the design of non-covalent inhibitors and have used, to this end, REINVENT 2.0, 20 an AI tool for de novo drug design developed by Thomas Blaschke et al The generative engine of the tool is directed by a reinforcement learning (RL) module.…”

“…14 Another similar approach employing reinforcement learning has been applied retrospectively to demonstrate that a DGM could generate active molecules where these molecules were not included in the training dataset. 15 Finally, de novo design approaches, without reinforcement learning, were applied to discover new SARS-COV-2 Mpro inhibitors for which two approaches were experimentally validated 16,17 while two others were not. 18,19…”