马拉维克衍生物治疗HIV的计算研究:QSAR和分子对接方法

IF 1.4 4区 化学 Q4 PHYSICS, ATOMIC, MOLECULAR & CHEMICAL
R. Sayyadi Kordabadi, S. A. S. Hashemi, O. Alizadeh
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引用次数: 0

摘要

本研究采用4种不同的方法,对25种黄芪衍生物的构效关系进行了研究。遗传算法-人工神经网络(GA-ANN)和多元线性回归-帝国主义竞争算法(MLR-ICA)的组合在线性和非线性方法中都表现出优越的性能。该研究确定了特定的描述符,如原子范德华体积、极化率和原子质量,在遗传算法-人工神经网络(GA-ANN)生物活性评估方法中具有重要意义。在亲脂性方面,在多元线性回归-帝国主义竞争算法(MLR-ICA)方法中强调了与Verhaar藻类基线毒性和极化性相关的描述符。分子对接分析表明,含5uw受体的Maraviroc衍生物22的亲和力最低,但氢键数最多。蒙特卡罗技术利用CORAL软件,确定了与生物活性(-logIC50)和亲脂性(XLOGP)相关的基本分子特征。这些特征包括有分支的环的存在,sp2碳与环相连,双键的独占存在,氮与环相连,氮存在于双键中,氟原子与分支相连,分支存在,氮原子与环相连。研究发现,将GA-ANN、MLR-ICA、蒙特卡罗方法和分子对接相结合,可以增强对理化描述符与药物机制之间关系的理解,有助于新药的设计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Computational Studies on Maraviroc Derivatives for HIV Treatment: QSAR and Molecular Docking Approaches

Computational Studies on Maraviroc Derivatives for HIV Treatment: QSAR and Molecular Docking Approaches

This research utilized four different methods to investigate the structure-activity relationships of 25 derivatives of Maraviroc. The combination of Genetic Algorithms-Artificial Neural Networks (GA–ANN) and Multiple Linear Regression-Imperialist Competitive Algorithm (MLR-ICA) demonstrated superior performance among both linear and nonlinear methods. The study identified specific descriptors, such as atomic van der Waals volumes, polarizability, and atomic masses, as significant in the Genetic Algorithms–Artificial Neural Networks (GA-ANN) method for biological activity assessment. In terms of lipophilicity, descriptors related to Verhaar Algae base-line toxicity and polarizability were highlighted in the Multiple Linear Regression-Imperialist Competitive Algorithm (MLR-ICA) method. Molecular docking analysis revealed that Maraviroc derivative number 22 with 5UIW receptor exhibited the lowest affinity but the highest number of hydrogen bonds. The Monte Carlo technique, utilizing CORAL software, pinpointed essential molecular characteristics linked to both biological activity (–logIC50) and lipophilicity (XLOGP). These features encompassed the existence of cyclic rings with branching, sp2 carbon linked to a ring, exclusive presence of double bonds, Nitrogen attachment to cyclic rings, Nitrogen presence in double bonds, Fluorine atom connection to branching, presence of branching, and Nitrogen atom linkage to a ring. The research found that the combined use of GA-ANN, MLR-ICA, Monte Carlo method, and molecular docking can enhance understanding of the relationship between physico-chemical descriptors and drug mechanisms, aiding in the design of new drugs.

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来源期刊
Russian Journal of Physical Chemistry B
Russian Journal of Physical Chemistry B 化学-物理:原子、分子和化学物理
CiteScore
2.20
自引率
71.40%
发文量
106
审稿时长
4-8 weeks
期刊介绍: Russian Journal of Physical Chemistry B: Focus on Physics is a journal that publishes studies in the following areas: elementary physical and chemical processes; structure of chemical compounds, reactivity, effect of external field and environment on chemical transformations; molecular dynamics and molecular organization; dynamics and kinetics of photoand radiation-induced processes; mechanism of chemical reactions in gas and condensed phases and at interfaces; chain and thermal processes of ignition, combustion and detonation in gases, two-phase and condensed systems; shock waves; new physical methods of examining chemical reactions; and biological processes in chemical physics.
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