Article Preview
Top1. Introduction
2.1 million new persistent HIV infections occur each year, which require lifelong treatment resulting in an increase in the rate of drug-resistant mutations. To identify and develop new HIV inhibitors with innovative mechanisms, it requires constant and timely efforts. Ribonuclease H (RNase H) associated with HIV-1 reverse transcriptase is the only encoded viral enzyme that is considered an effective inhibitor, whereas it is a well-validated target whose functional repeal compromises the viral infectivity. The identification of new drugs is a long and costly process that can be accelerated by in silico methods (Corona A et al., 2013). The new subtype of 2-hydroxyisoquinoline-1,3-dione (HID) is capable of powerfully and selectively inhibiting RNase H without inhibiting HIV in cell culture (Wang L et al., 2018).
Currently, drug discovery has become one of the most promising areas in the pharmaceutical industry by using advanced and modern computer methods. This computer-aided discipline is revolutionizing the field of drug design. This has made it possible to predict the structures of compounds having the desirable characteristics, by increasing the chances that the drug obtained from this compound overcomes the barriers of preclinical tests (Shameera et al., 2019).
Two valuable techniques in computational chemistry that can provide very valuable information in the early stages of the drug design process are: molecular docking and quantitative structure -activity relationship (QSAR) (Karcher et al., 1990; Höltje et al., 1997). The prediction of biological activity and the understanding of ligand-receptor interactions in a three-dimensional way can be studied by QSAR analysis and molecular docking (Ousaa et al., 2018). The QSAR model is a mathematical equation that links the relationship between the chemical structure of a molecule and its biological activity. To find this equation, a number of chemical compounds with known activity values should be used, and for each chemical compound, a series of chemical descriptors must be computed. In this work, a series of 28 hydroxyisoquinoline-1,3-diones with ribonuclease H inhibition activity was used to establish 2D and 3D-QSAR models, with an objective is to find a statistically significant correlation between several molecular properties of all molecules and their known biological activity. Thus, to have a prediction of the antiviral activity of this series, we applied Surflex-Docking (Hornak et al., 1999) to determine the stability of the most active and least active molecule with its potential targets, and on the other hand, to study their interactions with the residues.