AI-Aided Design of Novel Targeted Covalent Inhibitors against SARS-CoV-2

The drug repurposing of known approved drugs (e.g., lopinavir/ritonavir) has failed to treat SARS-CoV-2-infected patients. Therefore, it is important to generate new chemical entities against this virus. As a critical enzyme in the lifecycle of the coronavirus, the 3C-like main protease (3CLpro or Mpro) is the most attractive target for antiviral drug design. Based on a recently solved structure (PDB ID: 6LU7), we developed a novel advanced deep Q-learning network with a fragment-based drug design (ADQN-FBDD) for generating potential lead compounds targeting SARS-CoV-2 3CLpro. We obtained a series of derivatives from the lead compounds based on our structure-based optimization policy (SBOP). All of the 47 lead compounds obtained directly with our AI model and related derivatives based on the SBOP are accessible in our molecular library. These compounds can be used as potential candidates by researchers to develop drugs against SARS-CoV-2.

Automated summary

In this study, a novel advanced deep Q-learning network with a fragment-based drug design (ADQN-FBDD) was developed to generate potential lead compounds targeting SARS-CoV-2 3CLpro. These compounds can be used as potential candidates by researchers to develop drugs against SARS-CoV-2.