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 (IC 50 ) in the range 0.18-18.82 μM. Cellular assays were also conducted to evaluate cytotoxic, haemolytic, and antiviral properties. A promising lead compound against coronavirus M pro was identified with dose-dependent inhibition of virus infectivity and minimal toxicity on human MRC-5 cells.
Keywords: Coronavirus; cheminformatics; drug discovery; ligand-based drug design; machine learning.
【저자키워드】 coronavirus, Drug discovery, ligand-based drug design, Machine learning., Cheminformatics, 【초록키워드】 drug design, Toxicity, Virtual screening, protease, in silico, binding affinity, cells, dataset, inhibitor, machine-learning, compounds, half-maximal inhibitory concentration, Compound, M pro, inhibitory effect, antiviral properties, virus infectivity, dose-dependent inhibition, thought, selected, evaluate, conducted, screened, 【제목키워드】 Protease inhibitor, molecular,