Faculty of Pharmacy, Al-Quds University, Jerusalem, Palestine.
Curr Pharm Des. 2013;19(12):2237-44. doi: 10.2174/1381612811319120010.
Quantitative structure-activity relationship study was performed to understand the inhibitory activity of a set of 192 vascular endothelial growth factor receptor-2 (VEGFR-2) compounds. QSAR models were developed using multiple linear regression (MLR) and partial least squares (PLS) as linear methods. While principal component - artificial neural networks (PC-ANN) modeling method with application of eigenvalue ranking factor selection procedure was used as nonlinear method. The results obtained offer good regression models having good prediction ability. The results obtained by MLR and PLS are close and better than those obtained by principal component- artificial neural network. The best model was obtained with a correlation coefficient of 0.87. The strength and the predictive performance of the proposed models was verified using both internal (cross-validation and Y-scrambling) and external statistical validations.
进行定量构效关系研究，以了解一组 192 种血管内皮生长因子受体-2（VEGFR-2）化合物的抑制活性。使用多元线性回归（MLR）和偏最小二乘（PLS）作为线性方法开发 QSAR 模型。而主成分-人工神经网络（PC-ANN）建模方法，应用特征值排序因子选择程序作为非线性方法。所得结果提供了具有良好预测能力的良好回归模型。MLR 和 PLS 得到的结果接近，优于主成分-人工神经网络得到的结果。用相关系数 0.87 得到最佳模型。通过内部（交叉验证和 Y 乱序）和外部统计验证来验证所提出模型的强度和预测性能。
