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

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.

Computational Approaches to Identify a Hidden Pharmacological Potential in Large Chemical Libraries

“…The data presented in Tab. 1 are close to the performance of QSAR models, which were also analyzed [66]. However, our results demonstrated for the first time the applicability of a similarity search using QNA and MNA descriptors as an effective method for processing large databases.…”

“…We had investigated the applicability of the proposed approach to the assessment of activity by similarity for 16,770 inhibitors of HIV-1 protease, reverse transcriptase, and integrase [66].…”

Report on an NIH Workshop on Ultralarge Chemistry Databases

“…52 A number of applications of General Unrestricted Structure-Activity Relationships (GUSAR) have been reported. 48,[53][54][55] To analyze ultralarge chemical databases, Poroikov and his colleagues have applied a complex computational approach, which combines structural similarity assessment, machine learning, and molecular modeling. 56 They have reported an approach for the identification of potential pharmacological substances in very large databases of a billion or more druglike compounds.…”

Recent Trends in Enzyme Inhibition and Activation in Drug Design

“…The authors created an overall training set including data from three databases NIAID, ChEMBL, and integrity for the development of the (Q)SAR models with the broadest coverage of chemical space. These (Q)SAR models were used for creating the web application that predicts anti-HIV activity [5].…”