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(Q)SAR Models of HIV-1 Protein Inhibition by Drug-Like Compounds

Despite the achievements of antiretroviral therapy, discovery of new anti-HIV medicines remains an essential task because the existing drugs do not provide a complete cure for the infected patients, exhibit severe adverse effects, and lead to the appearance of resistant strains. To predict the inter...

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Autores principales: Stolbov, Leonid A., Druzhilovskiy, Dmitry S., Filimonov, Dmitry A., Nicklaus, Marc C., Poroikov, Vladimir V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6983201/
https://www.ncbi.nlm.nih.gov/pubmed/31881687
http://dx.doi.org/10.3390/molecules25010087
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author Stolbov, Leonid A.
Druzhilovskiy, Dmitry S.
Filimonov, Dmitry A.
Nicklaus, Marc C.
Poroikov, Vladimir V.
author_facet Stolbov, Leonid A.
Druzhilovskiy, Dmitry S.
Filimonov, Dmitry A.
Nicklaus, Marc C.
Poroikov, Vladimir V.
author_sort Stolbov, Leonid A.
collection PubMed
description Despite the achievements of antiretroviral therapy, discovery of new anti-HIV medicines remains an essential task because the existing drugs do not provide a complete cure for the infected patients, exhibit severe adverse effects, and lead to the appearance of resistant strains. To predict the interaction of drug-like compounds with multiple targets for HIV treatment, ligand-based drug design approach is widely applied. In this study, we evaluated the possibilities and limitations of (Q)SAR analysis aimed at the discovery of novel antiretroviral agents inhibiting the vital HIV enzymes. Local (Q)SAR models are based on the analysis of structure–activity relationships for molecules from the same chemical class, which significantly restrict their applicability domain. In contrast, global (Q)SAR models exploit data from heterogeneous sets of drug-like compounds, which allows their application to databases containing diverse structures. We compared the information for HIV-1 integrase, protease and reverse transcriptase inhibitors available in the EBI ChEMBL, NIAID HIV/OI/TB Therapeutics, and Clarivate Analytics Integrity databases as the sources for (Q)SAR training sets. Using the PASS and GUSAR software, we developed and validated a variety of (Q)SAR models, which can be further used for virtual screening of new antiretrovirals in the SAVI library. The developed models are implemented in the freely available web resource AntiHIV-Pred.
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spelling pubmed-69832012020-02-06 (Q)SAR Models of HIV-1 Protein Inhibition by Drug-Like Compounds Stolbov, Leonid A. Druzhilovskiy, Dmitry S. Filimonov, Dmitry A. Nicklaus, Marc C. Poroikov, Vladimir V. Molecules Article Despite the achievements of antiretroviral therapy, discovery of new anti-HIV medicines remains an essential task because the existing drugs do not provide a complete cure for the infected patients, exhibit severe adverse effects, and lead to the appearance of resistant strains. To predict the interaction of drug-like compounds with multiple targets for HIV treatment, ligand-based drug design approach is widely applied. In this study, we evaluated the possibilities and limitations of (Q)SAR analysis aimed at the discovery of novel antiretroviral agents inhibiting the vital HIV enzymes. Local (Q)SAR models are based on the analysis of structure–activity relationships for molecules from the same chemical class, which significantly restrict their applicability domain. In contrast, global (Q)SAR models exploit data from heterogeneous sets of drug-like compounds, which allows their application to databases containing diverse structures. We compared the information for HIV-1 integrase, protease and reverse transcriptase inhibitors available in the EBI ChEMBL, NIAID HIV/OI/TB Therapeutics, and Clarivate Analytics Integrity databases as the sources for (Q)SAR training sets. Using the PASS and GUSAR software, we developed and validated a variety of (Q)SAR models, which can be further used for virtual screening of new antiretrovirals in the SAVI library. The developed models are implemented in the freely available web resource AntiHIV-Pred. MDPI 2019-12-25 /pmc/articles/PMC6983201/ /pubmed/31881687 http://dx.doi.org/10.3390/molecules25010087 Text en © 2019 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Stolbov, Leonid A.
Druzhilovskiy, Dmitry S.
Filimonov, Dmitry A.
Nicklaus, Marc C.
Poroikov, Vladimir V.
(Q)SAR Models of HIV-1 Protein Inhibition by Drug-Like Compounds
title (Q)SAR Models of HIV-1 Protein Inhibition by Drug-Like Compounds
title_full (Q)SAR Models of HIV-1 Protein Inhibition by Drug-Like Compounds
title_fullStr (Q)SAR Models of HIV-1 Protein Inhibition by Drug-Like Compounds
title_full_unstemmed (Q)SAR Models of HIV-1 Protein Inhibition by Drug-Like Compounds
title_short (Q)SAR Models of HIV-1 Protein Inhibition by Drug-Like Compounds
title_sort (q)sar models of hiv-1 protein inhibition by drug-like compounds
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6983201/
https://www.ncbi.nlm.nih.gov/pubmed/31881687
http://dx.doi.org/10.3390/molecules25010087
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