Integrative approaches in <scp>HIV</scp>‐1 non‐nucleoside reverse transcriptase inhibitor design

Abstract

The design of inhibitors for human immunodeficiency virus type‐1 reverse transcriptase ( HIV ‐1 RT ) is one of the most successful approaches for the treatment of HIV infections. Among the HIV ‐1 RT inhibitors, non‐nucleoside reverse transcriptase inhibitors ( NNRTIs ) constitute a prominent drug class, which includes nevirapine, delavirdine, efavirenz, etravirine, and rilpivirine approved for clinical use. However, the efficiency of many of these drugs has been undermined by drug‐resistant variants of HIV ‐1 RT , and it therefore becomes inevitable to design novel drugs to cope with resistance. Here, we discuss various drug design strategies, which include traditional medicinal chemistry, computational chemistry, and chemical biology approaches. In particular, computational modeling approaches, including machine learning, empirical descriptors‐based, force‐field, ab initio , and hybrid quantum mechanics/molecular mechanics‐based methods are discussed in detail. We foresee that these methods will have a major impact on efforts to guide the design and discovery of the next generation of NNRTIs that combat RT multidrug resistance. WIREs Comput Mol Sci 2018, 8:e1328. doi: 10.1002/wcms.1328 This article is categorized under: Structure and Mechanism &gt; Computational Biochemistry and Biophysics Computer and Information Science &gt; Chemoinformatics Molecular and Statistical Mechanics &gt; Free Energy Methods