ArticleEuropean journal of medicinal chemistry2022
Discovery of novel SARS-CoV-2 3CL protease covalent inhibitors using deep learning-based screen.
Article in European journal of medicinal chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 21 citations in OpenAlex.
- On the origins of SARS-CoV-2 main protease inhibitors.RSC medicinal chemistry · 2024Review
- Structure and function of SARS-CoV and SARS-CoV-2 main proteases and their inhibition: A comprehensive review.European journal of medicinal chemistry · 2023Review
- Entropy driven cooperativity effect in multi-site drug optimization targeting SARS-CoV-2 papain-like protease.Cellular and molecular life sciences : CMLS · 2023Article
- Recent Advances in Covalent Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2023Review
- Multiple machine learning methods aided virtual screening of NaJournal of cellular and molecular medicine · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 IC
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Registered trials
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