Al-Quds University, Jerusalem, Palestine.
Chem Biol Drug Des. 2010 May;75(5):506-14. doi: 10.1111/j.1747-0285.2010.00953.x.
The support vector machine (SVM) and partial least square (PLS) methods were used to develop quantitative structure activity relationship (QSAR) models to predict the inhibitory activity of non-peptide HIV-1 protease inhibitors. Genetic algorithm (GA) was employed to select variables that lead to the best-fitted models. A comparison between the obtained results using SVM with those of PLS revealed that the SVM model is much better than that of PLS. The root mean square errors of the training set and the test set for SVM model were calculated to be 0.2027, 0.2751, and the coefficients of determination (R(2)) are 0.9800, 0.9355 respectively. Furthermore, the obtained statistical parameter of leave-one-out cross-validation test (Q(2)) on SVM model was 0.9672, which proves the reliability of this model. The results suggest that TE2, Ui, GATS5e, Mor13e, ATS7m, Ss, Mor27e, and RDF035e are the main independent factors contributing to the inhibitory activities of the studied compounds.
支持向量机 (SVM) 和偏最小二乘法 (PLS) 方法被用于建立定量构效关系 (QSAR) 模型，以预测非肽类 HIV-1 蛋白酶抑制剂的抑制活性。遗传算法 (GA) 被用于选择能够得到最佳拟合模型的变量。SVM 与 PLS 得到的结果进行比较，结果表明 SVM 模型比 PLS 模型要好得多。SVM 模型的训练集和测试集的均方根误差分别计算为 0.2027 和 0.2751，决定系数 (R(2)) 分别为 0.9800 和 0.9355。此外，SVM 模型的留一交叉验证测试 (Q(2)) 的统计参数为 0.9672，证明了该模型的可靠性。结果表明，TE2、Ui、GATS5e、Mor13e、ATS7m、Ss、Mor27e 和 RDF035e 是影响研究化合物抑制活性的主要独立因素。
