Luan F, Liu H T, Ma W P, Fan B T
Department of Applied Chemistry, Yantai University, Yantai, Shandong 264005, PR China.
Eur J Med Chem. 2008 Jan;43(1):43-52. doi: 10.1016/j.ejmech.2007.03.002. Epub 2007 Mar 18.
Classification models of estrogen receptor-beta ligands were proposed using linear and nonlinear models. The data set was divided into active and inactive classes on the basis of their binding affinities. The two-class problem (active, inactive) was firstly explored by linear classifier approach, linear discriminant analysis (LDA). In order to get a more accurate prediction model, the nonlinear novel machine learning technique, support vectors machine (SVM), was subsequently used to investigate. The heuristic method (HM) was used to pre-select the whole descriptor sets. The model containing eight descriptors founded by SVM, showed better predictive ability than LDA. The accuracy in prediction for the training, test and overall data sets are 92.9%, 85.8% and 91.4% for SVM, 83.1%, 76.1% and 81.9% for LDA, respectively. The results indicate that SVM can be used as a powerful modeling tool for QSAR studies.
利用线性和非线性模型提出了雌激素受体-β配体的分类模型。根据结合亲和力将数据集分为活性和非活性类别。首先采用线性分类器方法——线性判别分析（LDA）来探讨两类问题（活性、非活性）。为了获得更准确的预测模型，随后使用非线性新型机器学习技术——支持向量机（SVM）进行研究。启发式方法（HM）用于预先选择整个描述符集。由支持向量机建立的包含八个描述符的模型显示出比线性判别分析更好的预测能力。支持向量机对训练集、测试集和总体数据集的预测准确率分别为92.9%、85.8%和91.4%，线性判别分析的相应准确率分别为83.1%、76.1%和81.9%。结果表明，支持向量机可作为定量构效关系研究的强大建模工具。
