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AI-aided design of novel targeted covalent inhibitors against SARS-CoV-2

Bowen Tang, Fengming He, Dongpeng Liu, Meijuan Fang, Zhen Wu, View ORCID ProfileDong Xu
doi: https://doi.org/10.1101/2020.03.03.972133
Bowen Tang
1Fujian Provincial Key Laboratory of Innovative Drug Target Research, School of Pharmaceutical Sciences, Xiamen University, Xiamen 361000, China
2Department of Electrical Engineering and Computer Science, Informatics Institute, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
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Fengming He
1Fujian Provincial Key Laboratory of Innovative Drug Target Research, School of Pharmaceutical Sciences, Xiamen University, Xiamen 361000, China
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Dongpeng Liu
2Department of Electrical Engineering and Computer Science, Informatics Institute, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
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Meijuan Fang
1Fujian Provincial Key Laboratory of Innovative Drug Target Research, School of Pharmaceutical Sciences, Xiamen University, Xiamen 361000, China
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Zhen Wu
1Fujian Provincial Key Laboratory of Innovative Drug Target Research, School of Pharmaceutical Sciences, Xiamen University, Xiamen 361000, China
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  • For correspondence: wuzhen@xmu.edu.cn xudong@missouri.edu
Dong Xu
2Department of Electrical Engineering and Computer Science, Informatics Institute, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
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  • ORCID record for Dong Xu
  • For correspondence: wuzhen@xmu.edu.cn xudong@missouri.edu
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Abstract

The focused drug repurposing of known approved drugs (such as lopinavir/ritonavir) has been reported failed for curing SARS-CoV-2 infected patients. It is urgent to generate new chemical entities against this virus. As a key enzyme in the life-cycle of coronavirus, the 3C-like main protease (3CLpro or Mpro) is the most attractive for antiviral drug design. Based on a recently solved structure (PDB ID: 6LU7), we developed a novel advanced deep Q-learning network with the fragment-based drug design (ADQN-FBDD) for generating potential lead compounds targeting SARS-CoV-2 3CLpro. We obtained a series of derivatives from those lead compounds by our structure-based optimization policy (SBOP). All the 47 lead compounds directly from our AI-model and related derivatives based on SBOP are accessible in our molecular library at https://github.com/tbwxmu/2019-nCov. These compounds can be used as potential candidates for researchers in their development of drugs against SARS-CoV-2.

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  • https://github.com/tbwxmu/2019-nCov

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Posted March 08, 2020.
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AI-aided design of novel targeted covalent inhibitors against SARS-CoV-2
Bowen Tang, Fengming He, Dongpeng Liu, Meijuan Fang, Zhen Wu, Dong Xu
bioRxiv 2020.03.03.972133; doi: https://doi.org/10.1101/2020.03.03.972133
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AI-aided design of novel targeted covalent inhibitors against SARS-CoV-2
Bowen Tang, Fengming He, Dongpeng Liu, Meijuan Fang, Zhen Wu, Dong Xu
bioRxiv 2020.03.03.972133; doi: https://doi.org/10.1101/2020.03.03.972133

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