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

• Published in: European journal of medicinal chemistry 2022-12, Vol.244, p.114803-114803, Article 114803
• Main Authors: Wang, Liying, Yu, Zhongtian, Wang, Shiwei, Guo, Zheng, Sun, Qi, Lai, Luhua
• Format: Article
• Language: English
• Subjects: 3C-like protease inhibitors; Antiviral Agents - pharmacology; Coronavirus 3C Proteases; Covalent warheads; COVID-19 Drug Treatment; Cysteine; Deep Learning; Humans; Protease Inhibitors - chemistry; Protease Inhibitors - pharmacology; Research Paper; SARS-CoV-2
• ISSN: 0223-5234; 1768-3254
• DOI: 10.1016/j.ejmech.2022.114803

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. [Display omitted] •Development of a generally applicable deep learning-based stepwise strategy for covalent inhibitor discovery.•12 compounds against the SARS-CoV-2 3CLpro identified by our deep learning-based stepwise strategy.•6 compounds with new scaffolds exhibited low micromolar binding potency.•5 of the 6 active compounds showed typical covalent inhibition behavior.