Discovery of novel SARS-CoV-2 3CL protease covalent inhibitors using deep learning-based screen.

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 µM and the strongest one inhibited SARS-CoV-2 3CL protease with an IC50 of 1.4 µM. 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.

Discovery of novel SARS-CoV-2 3CL protease covalent inhibitors using deep learning-based screen

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 μM and the strongest one inhibited SARS-CoV-2 3CL protease with an IC50 of 1.4 μM. 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. Graphical Image 1

Discovery of 2-(furan-2-ylmethylene)hydrazine-1-carbothioamide derivatives as novel inhibitors of SARS-CoV-2 main protease.

The COVID-19 posed a serious threat to human life and health, and SARS-CoV-2 Mpro has been considered as an attractive drug target for the treatment of COVID-19. Herein, we report 2-(furan-2-ylmethylene)hydrazine-1-carbothioamide derivatives as novel inhibitors of SARS-CoV-2 Mpro developed by in-house library screening and biological evaluation. Similarity search led to the identification of compound F8-S43 with the enzymatic IC50 value of 10.76 µM. Further structure-based drug design and synthetic optimization uncovered compounds F8-B6 and F8-B22 as novel non-peptidomimetic inhibitors of Mpro with IC50 values of 1.57 µM and 1.55 µM, respectively. Moreover, enzymatic kinetic assay and mass spectrometry demonstrated that F8-B6 was a reversible covalent inhibitor of Mpro. Besides, F8-B6 showed low cytotoxicity with CC50 values of more than 100 µM in Vero and MDCK cells. Overall, these novel SARS-CoV-2 Mpro non-peptidomimetic inhibitors provide a useful starting point for further structural optimization.