期刊
EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY
卷 244, 期 -, 页码 -出版社
ELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER
DOI: 10.1016/j.ejmech.2022.114803
关键词
SARS-CoV-2; 3C-like protease inhibitors; Deep learning; Covalent warheads
资金
- Chinese Academy of Medical Sciences [2021-I2M-5-014]
- Ministry of Science and Technology of China [2016YFA0502303]
A deep learning-based stepwise strategy was developed to selectively screen for highly active covalent inhibitors of SARS-CoV-2 3CL protease, providing new possibilities for drug development against COVID-19.
SARS-CoV-2 3CL protease is one of the key targets for drug development against COVID-19. Most known SARS-CoV-2 3CL protease inhibitors act by covalently binding to the active site cysteine. Yet, computational screens against this enzyme were mainly focused on non-covalent inhibitor discovery. Here, we developed a deep learning-based stepwise strategy for selective covalent inhibitor screen. We used a deep learning framework that integrated a directed message passing neural network with a feed-forward neural network to construct two different classifiers for either covalent or non-covalent inhibition activity prediction. These two classifiers were trained on the covalent and non-covalent 3CL protease inhibitors dataset, respectively, which achieved high prediction accuracy. We then successively applied the covalent inhibitor model and the non-covalent inhibitor model to screen a chemical library containing compounds with covalent warheads of cysteine. We experimentally tested the inhibition activity of 32 top-ranking compounds and 12 of them were active, among which 6 showed IC50 values less than 12 mu M and the strongest one inhibited SARS-CoV-2 3CL protease with an IC50 of 1.4 mu M. Further investigation demonstrated that 5 of the 6 active compounds showed typical covalent inhibition behavior with time-dependent activity. These new covalent inhibitors provide novel scaffolds for developing highly active SARS-CoV-2 3CL covalent inhibitors.
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