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探索作为HIV-1整合酶抑制剂的苯乙烯基喹啉衍生物的分子形状分析。

Exploring molecular shape analysis of styrylquinoline derivatives as HIV-1 integrase inhibitors.

作者信息

Leonard J Thomas, Roy Kunal

机构信息

Drug Theoretics and Cheminformatics Lab, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Raja S C Mullick Road, Kolkata, West Bengal 700 032, India.

出版信息

Eur J Med Chem. 2008 Jan;43(1):81-92. doi: 10.1016/j.ejmech.2007.02.021. Epub 2007 Mar 14.

Abstract

HIV-1 integrase inhibitory activity data of styrylquinoline derivatives have been subjected to 3D-QSAR study by molecular shape analysis (MSA) technique using Cerius(2) version 4.8 software (Accelrys). For the selection of test set compounds, initially a QSAR analysis was done based on topological and structural descriptors and K-means clustering technique was used to classify the entire data set (n=36). Clusters were formed from the factor scores of the whole data set comprising of topological and structural descriptors without the biological activity, and based on the clusters, the data set was divided into training and test sets (n=26 and n=10, respectively) so that all clusters are properly represented in both training and test sets. In the molecular shape analysis, the major steps were (1) generation of conformers and energy minimization; (2) hypothesizing an active conformer (global minimum of the most active compound); (3) selecting a candidate shape reference compound (based on active conformation); (4) performing pair-wise molecular superimposition using maximum common subgroup [MCSG] method; (5) measuring molecular shape commonality using MSA descriptors; (6) determination of other molecular features by calculating spatial and conformational parameters; (7) selection of conformers; (8) generation of QSAR equations by standard statistical techniques. The best model obtained from stepwise regression and GFA techniques shows 51.6% predicted variance (leave-one-out) and 57.3% explained variance. In case of FA-PLS regression, the best relation shows 54.0% predicted variance and 57.9% explained variance. The R(2)(pred) and R(2)(test) values for the GFA derived model are 0.611 and 0.664, respectively, while the best FA-PLS model has R(2)(pred) and R(2)(test) values of 0.602 and 0.656, respectively. These models show the importance of Jurs descriptors (total polar surface area, relative polar surface area, relative hydrophobic surface area, relative positive charge), fraction area of the molecular shadow in the XZ plane (ShadowXZfrac), common overlap steric volume and the ratio of common overlap steric volume to volume of individual molecules. Statistically reliable MSA models obtained from this study suggest that this technique could be useful to design potent HIV-1 integrase inhibitors.

摘要

已使用Cerius(2) 4.8版软件(Accelrys公司)通过分子形状分析(MSA)技术对苯乙烯基喹啉衍生物的HIV-1整合酶抑制活性数据进行了三维定量构效关系(3D-QSAR)研究。为了选择测试集化合物,最初基于拓扑和结构描述符进行了定量构效关系分析,并使用K均值聚类技术对整个数据集(n = 36)进行分类。聚类是根据由不含生物活性的拓扑和结构描述符组成的整个数据集的因子得分形成的,基于这些聚类,将数据集分为训练集和测试集(分别为n = 26和n = 10),以便在训练集和测试集中都能恰当地体现所有聚类。在分子形状分析中,主要步骤包括:(1)生成构象异构体并进行能量最小化;(2)假设一个活性构象异构体(最具活性化合物的全局最小值);(3)选择一个候选形状参考化合物(基于活性构象);(4)使用最大公共子群[MCSG]方法进行成对分子叠合;(5)使用MSA描述符测量分子形状相似性;(6)通过计算空间和构象参数确定其他分子特征;(7)选择构象异构体;(8)通过标准统计技术生成定量构效关系方程。从逐步回归和广义因子分析(GFA)技术获得的最佳模型显示预测方差为51.6%(留一法),解释方差为57.3%。在因子分析偏最小二乘(FA-PLS)回归中,最佳关系显示预测方差为54.0%,解释方差为57.9%。GFA衍生模型的R(2)(pred)和R(2)(test)值分别为0.611和0.664,而最佳FA-PLS模型的R(2)(pred)和R(2)(test)值分别为0.602和0.656。这些模型显示了朱尔斯描述符(总极性表面积、相对极性表面积、相对疏水表面积、相对正电荷)、分子在XZ平面上的阴影部分面积分数(ShadowXZfrac)、公共重叠空间体积以及公共重叠空间体积与单个分子体积之比的重要性。从本研究中获得的统计上可靠的MSA模型表明,该技术可能有助于设计有效的HIV-1整合酶抑制剂。

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