首页> 外文期刊>Medicinal chemistry research: an international journal for rapid communications on design and mechanisms of action of biologically active agents >Statistically validated QSAR study-of-some antagonists of the human CCR5 receptor using least square support vector machine based on the genetic algorithm and factor analysis
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Statistically validated QSAR study-of-some antagonists of the human CCR5 receptor using least square support vector machine based on the genetic algorithm and factor analysis

机译:基于最小二乘支持向量机的遗传算法和因子分析,对经统计验证的人CCR5受体某些拮抗剂的QSAR研究

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摘要

Quantitative relationships between molecular structures and CCR5 inhibitory activities of forty two l-amino-2-phenyl-4-(piperidin-l-yl)-butane derivatives were discovered by chemometrics tools including GA-MLR and FA-MLR as linear models and GA-LS-SVM and FA-LS-SVM as nonlinear models. GA-MLR analysis indicated that the topological (X2A) and geometrical (MAXDN) parameters have the most significant influence on the CCR5 inhibitory activity. FA-MLR model describes the effect of topological, geometrical, and quantum indices on the CCR5 inhibitory activity of the studied molecules. A comparison between the developed statistical methods revealed that FA-LS-SVM represented superior results and it could predict about 95 % of variance in the inhibitory activity data. The resulted models were validated for generalization and predictability by leave-one-out (LOO) cross-validation method. External validation showed high predictability of the calibration models. The predictability of the obtained QSAR models was also investigated by the Tropsha and Roy proposed criteria. Further validation by F-randomization method confirmed that the obtained models were not due to a chance correlation. The applicability domain of the models was defined by leverage value. None of the studied compounds were outside the domain of the models.
机译:通过化学计量学工具(包括线性模型GA-MLR和FA-MLR和GA)发现了42种l-氨基-2-苯基-4-(哌啶-1-基)丁烷衍生物的分子结构与CCR5抑制活性之间的定量关系。 -LS-SVM和FA-LS-SVM作为非线性模型。 GA-MLR分析表明,拓扑参数(X2A)和几何参数(MAXDN)对CCR5抑制活性的影响最大。 FA-MLR模型描述了拓扑,几何和量子指数对所研究分子的CCR5抑制活性的影响。所开发的统计方法之间的比较表明,FA-LS-SVM代表了优异的结果,并且可以预测抑制活性数据中约95%的差异。通过留一法(LOO)交叉验证方法验证了所得模型的通用性和可预测性。外部验证显示出校准模型的高度可预测性。 Tropsha和Roy提出的标准还研究了获得的QSAR模型的可预测性。通过F随机化方法的进一步验证证实,所获得的模型不是由于机会相关性造成的。模型的适用范围由杠杆值定义。所研究的化合物均未超出模型范围。

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