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AI-Aided Design of Novel Targeted Covalent Inhibitors against SARS-CoV-2
The drug repurposing of known approved drugs (e.g., lopinavir/ritonavir) has failed to treat SARS-CoV-2-infected patients. Therefore, it is important to generate new chemical entities against this virus. As a critical enzyme in the lifecycle of the coronavirus, the 3C-like main protease (3CLpro or M...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9220321/ https://www.ncbi.nlm.nih.gov/pubmed/35740872 http://dx.doi.org/10.3390/biom12060746 |
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author | Tang, Bowen He, Fengming Liu, Dongpeng He, Fei Wu, Tong Fang, Meijuan Niu, Zhangming Wu, Zhen Xu, Dong |
author_facet | Tang, Bowen He, Fengming Liu, Dongpeng He, Fei Wu, Tong Fang, Meijuan Niu, Zhangming Wu, Zhen Xu, Dong |
author_sort | Tang, Bowen |
collection | PubMed |
description | The drug repurposing of known approved drugs (e.g., lopinavir/ritonavir) has failed to treat SARS-CoV-2-infected patients. Therefore, it is important to generate new chemical entities against this virus. As a critical enzyme in the lifecycle of the coronavirus, the 3C-like main protease (3CLpro or Mpro) is the most attractive target for antiviral drug design. Based on a recently solved structure (PDB ID: 6LU7), we developed a novel advanced deep Q-learning network with a fragment-based drug design (ADQN–FBDD) for generating potential lead compounds targeting SARS-CoV-2 3CLpro. We obtained a series of derivatives from the lead compounds based on our structure-based optimization policy (SBOP). All of the 47 lead compounds obtained directly with our AI model and related derivatives based on the SBOP are accessible in our molecular library. These compounds can be used as potential candidates by researchers to develop drugs against SARS-CoV-2. |
format | Online Article Text |
id | pubmed-9220321 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92203212022-06-24 AI-Aided Design of Novel Targeted Covalent Inhibitors against SARS-CoV-2 Tang, Bowen He, Fengming Liu, Dongpeng He, Fei Wu, Tong Fang, Meijuan Niu, Zhangming Wu, Zhen Xu, Dong Biomolecules Article The drug repurposing of known approved drugs (e.g., lopinavir/ritonavir) has failed to treat SARS-CoV-2-infected patients. Therefore, it is important to generate new chemical entities against this virus. As a critical enzyme in the lifecycle of the coronavirus, the 3C-like main protease (3CLpro or Mpro) is the most attractive target for antiviral drug design. Based on a recently solved structure (PDB ID: 6LU7), we developed a novel advanced deep Q-learning network with a fragment-based drug design (ADQN–FBDD) for generating potential lead compounds targeting SARS-CoV-2 3CLpro. We obtained a series of derivatives from the lead compounds based on our structure-based optimization policy (SBOP). All of the 47 lead compounds obtained directly with our AI model and related derivatives based on the SBOP are accessible in our molecular library. These compounds can be used as potential candidates by researchers to develop drugs against SARS-CoV-2. MDPI 2022-05-25 /pmc/articles/PMC9220321/ /pubmed/35740872 http://dx.doi.org/10.3390/biom12060746 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Tang, Bowen He, Fengming Liu, Dongpeng He, Fei Wu, Tong Fang, Meijuan Niu, Zhangming Wu, Zhen Xu, Dong AI-Aided Design of Novel Targeted Covalent Inhibitors against SARS-CoV-2 |
title | AI-Aided Design of Novel Targeted Covalent Inhibitors against SARS-CoV-2 |
title_full | AI-Aided Design of Novel Targeted Covalent Inhibitors against SARS-CoV-2 |
title_fullStr | AI-Aided Design of Novel Targeted Covalent Inhibitors against SARS-CoV-2 |
title_full_unstemmed | AI-Aided Design of Novel Targeted Covalent Inhibitors against SARS-CoV-2 |
title_short | AI-Aided Design of Novel Targeted Covalent Inhibitors against SARS-CoV-2 |
title_sort | ai-aided design of novel targeted covalent inhibitors against sars-cov-2 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9220321/ https://www.ncbi.nlm.nih.gov/pubmed/35740872 http://dx.doi.org/10.3390/biom12060746 |
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