Abstract
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.
Highlights
Discovery of novel pharmaceutical agents is always based on existing knowledge about the mechanisms of the disease—molecular targets that should be affected to normalize the pathological process and ligands that may interact with the targets of interest [1]
We exported the data for HIV-1 IN, PR, and reverse transcriptase (RT) inhibitors from the NIAID, ChEMBL, and Integrity databases
For creating (Q)SAR models one may use as training sets the combined sets from three pairs of databases and validate these models on the basis of independent test sets representing the unique part of the third database
Summary
Discovery of novel pharmaceutical agents is always based on existing knowledge about the mechanisms of the disease—molecular targets that should be affected to normalize the pathological process and ligands that may interact with the targets of interest [1]. No investigational drug is studied without some contribution of in silico methods [2]. When information about the three-dimensional structure of the target is known, Structure-Based Drug Design (SBDD) methods are applied for virtual screening in databases of available samples or de novo design of potential ligands with the subsequent synthesis and testing of activity in vitro. Despite the recognized limitations of SBDD methods [3], dozens of newly launched drugs have been developed using this approach [4], which is utilized to overcome a target’s drug resistance [5]. The first computational methodology applied to research and development of new drugs was Ligand-Based Drug Design. Starting from the analysis of Quantitative Structure–Activity Relationships (QSAR)
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