Department of Medicinal Chemistry, Faculty of Pharmacy, Isfahan University of Medical Sciences, 81746-73461 Isfahan, Iran.
Eur J Med Chem. 2010 Apr;45(4):1572-82. doi: 10.1016/j.ejmech.2009.12.066. Epub 2010 Jan 28.
Principal component regression (PCR), principal component-artificial neural network (PC-ANN), and principal component-least squares-support vector machine (PC-LS-SVM) as regression methods were investigated for building quantitative structure-activity relationships for the prediction of inhibitory activity of some CCR1 antagonists. Nonlinear methods (PC-ANN and PC-LS-SVM) were better than the PCR method considerably in the goodness of fit and predictivity parameters and other criteria for evaluation of the proposed model. These results reflect a nonlinear relationship between the principal components obtained from molecular descriptors and the inhibitory activity of this set of molecules. The maximum variance in activity of the molecules, in PCR method was 45.5%, whereas nonlinear methods, PC-ANN and PC-LS-SVM, could explain more than 93.7% and 95.6% variance in activity data respectively.
主成分回归（PCR）、主成分-人工神经网络（PC-ANN）和主成分-最小二乘支持向量机（PC-LS-SVM）作为回归方法，用于建立一些 CCR1 拮抗剂抑制活性的定量构效关系预测。非线性方法（PC-ANN 和 PC-LS-SVM）在拟合优度和预测性参数以及其他评价模型的标准方面，明显优于 PCR 方法。这些结果反映了从分子描述符中得到的主成分与这组分子的抑制活性之间的非线性关系。在 PCR 方法中，分子活性的最大方差为 45.5%，而非线性方法 PC-ANN 和 PC-LS-SVM 分别可以解释活性数据中超过 93.7%和 95.6%的方差。
