Niu Bing, Lu Wen-cong, Yang Shan-sheng, Cai Yu-dong, Li Guo-zheng
College of Material Science and Engineering, Shanghai University, Shanghai 200444, China.
Acta Pharmacol Sin. 2007 Jul;28(7):1075-86. doi: 10.1111/j.1745-7254.2007.00573.x.
To discriminate 32 phenethyl-amines between antagonists and agonists, and predict the activities of these compounds.
The support vector machine (SVM) is employed to investigate the structure-activity relationship (SAR)/quantitative structure-activity relationship (QSAR) of phenethyl-amines based on molecular descriptors.
By using the leave-one-out cross-validation (LOOCV) test, 1 optimal SAR and 2 optimal QSAR models for agonists and antagonists were attained. The accuracy of prediction for the classification of phenethyl-amines by using the LOOCV test is 91.67%, and the accuracy of prediction for the classification of phenethyl-amines by using the independent test is 100%; the results are better than those of the Fisher, the artificial neural network (ANN), and the K-nearest neighbor models for this real world data. The RMSE (root mean square error) of antagonists' QSAR model is 0.5881, and the RMSE of agonists' QSAR model is 0.4779, which are better than those of the multiple linear regression, partial least squares, and ANN models for this real world data.
The SVM can be used to investigate the SAR and QSAR of phenethylamines and could be a promising tool in the field of SAR/QSAR research.
区分32种苯乙胺类化合物的拮抗剂和激动剂，并预测这些化合物的活性。
基于分子描述符，采用支持向量机（SVM）研究苯乙胺类化合物的构效关系（SAR）/定量构效关系（QSAR）。
通过留一法交叉验证（LOOCV）测试，获得了1个针对激动剂和拮抗剂的最佳SAR模型以及2个最佳QSAR模型。使用LOOCV测试对苯乙胺类化合物进行分类的预测准确率为91.67%，使用独立测试对苯乙胺类化合物进行分类的预测准确率为100%；对于该实际数据，这些结果优于Fisher模型、人工神经网络（ANN）模型和K近邻模型。拮抗剂QSAR模型的均方根误差（RMSE）为0.5881，激动剂QSAR模型的RMSE为0.4779，对于该实际数据，这些结果优于多元线性回归模型、偏最小二乘模型和ANN模型。
支持向量机可用于研究苯乙胺类化合物的SAR和QSAR，在SAR/QSAR研究领域可能是一种很有前景的工具。
