Cargando…
(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...
Autores principales: | , , , , |
---|---|
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 |
_version_ | 1783491465560719360 |
---|---|
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. |
format | Online Article Text |
id | pubmed-6983201 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT stolbovleonida qsarmodelsofhiv1proteininhibitionbydruglikecompounds AT druzhilovskiydmitrys qsarmodelsofhiv1proteininhibitionbydruglikecompounds AT filimonovdmitrya qsarmodelsofhiv1proteininhibitionbydruglikecompounds AT nicklausmarcc qsarmodelsofhiv1proteininhibitionbydruglikecompounds AT poroikovvladimirv qsarmodelsofhiv1proteininhibitionbydruglikecompounds |