Ligand-based discovery of coronavirus main protease inhibitors using MACAW molecular embeddings.

ABSTRACT: Ligand-based drug design methods are thought to require large experimental datasets to become useful for virtual screening. In this work, we propose a computational strategy to design novel inhibitors of coronavirus main protease, M^pro. The pipeline integrates publicly available screening and binding affinity data in a two-stage machine-learning model using the recent MACAW embeddings. Once trained, the model can be deployed to rapidly screen large libraries of molecules in silico. Several hundred thousand compounds were virtually screened and 10 of them were selected for experimental testing. From these 10 compounds, 8 showed a clear inhibitory effect on recombinant M^pro, with half-maximal inhibitory concentration values (IC₅₀) in the range 0.18-18.

SUBMITTER: Dong J 

PROVIDER: S-EPMC9621234 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature