Quantitative Structure Activity Relationship Analysis of Antiviral Activity of PF74 Type HIV-1 Capsid Protein Inhibitors by Simplex Representation of Molecular Structure

数量结构-活动关系 化学 衣壳 单纯疱疹病毒 人类免疫缺陷病毒(HIV) 计算生物学 分子模型 对接(动物) 分子 单纯形 分子描述符 立体化学 病毒 生物化学 病毒学 有机化学 生物 基因 护理部 医学 数学 几何学
作者
Bruno Louis,Vijay K. Agrawal
出处
期刊:Polycyclic Aromatic Compounds [Taylor & Francis]
卷期号:43 (2): 1845-1860
标识
DOI:10.1080/10406638.2022.2038215
摘要

Human immunodeficiency virus type 1 (HIV-1) capsid protein (CA) plays essential roles in both early and late stages of HIV-1 replication and it is an important target for developing new therapeutic drugs. In the present study, quantitative structure–activity relationships (QSARs) models were developed for a series of 46 derivatives of PF74 CA inhibitors binding to the HIV1 capsid. The Partial least square (PLS) method was applied to obtain QSAR models based on simplex representation of molecular structure (SiRMS). Molecular docking studies were performed to find the optimal conformation of molecules within the binding site, and to compute 3D SiRMS descriptors. The study produced several statistically significant and acceptable QSAR models (R2 = 0.98, Q2 = 0.76–0.78, R2 predict = 0.84). A consensus model developed from the five excellent PLS QSAR models which was statistically validated for its predictive power using cross-validation, Y-randomization, and applicability domain evaluation procedures. The obtained results have satisfactory statistical parameters within acceptable range. The model interpreted by a reverse analysis identified the molecular fragments that promote and interfere with antiviral activity and the degree to which they influence activity. The influence of the physical–chemical properties of the molecules on antiviral activity was determined. It has been observed that antiviral activity is mainly governed by electrostatic characteristics of molecular structure and hydrophobic factors. The results obtained may further assist in the design of new CA inhibitor compounds and prediction of their activity.
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