Support vector machine applied in QSAR modelling

作     者：MEI Hu ZHOU Yuan LIANG Guizhao LI Zhiliang 

作者机构：College of Chemistry and Chemical Engineering Chongqing University Chongqing 400044 China Key Laboratory of Biomedical Engineering of Ministry of Education and Chongqing City Chongqing 400044 China College of Bioengineering. Chongqing University Chongqing 400044 China 

出 版 物：《Chinese Science Bulletin》 (中国科学通报)

年 卷 期：2005年第50卷第20期

页      面：2291-2296页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by the Fok-Yingtung Educational Foundation(FYEF)(Grant No.98-7-6) the National Chun-hui Project Foundation(NCPF)(Grant No.99-04+99-37) Chongqing Applied Fundamental Science Fund(CAFS)(Grant No.01-3-6) 

主　　题：支撑向量装置 SVM 最小二乘法 QSAR模型 人工神经网络 

摘      要：Support vector machine (SVM), partial least squares (PLS), and Back-Propagation artificial neural net- work (ANN) were employed to establish QSAR models of 2 dipeptide datasets. In order to validate predictive capabilities on external dataset of the resulting models, both internal and external validations were performed. The division of dataset into both training and test sets was carried out by D-optimal design. The results showed that support vector machine (SVM) behaved well in both calibration and prediction. For the dataset of 48 bitter tasting dipeptides (BTD), the results obtained by support vector regression (SVR) were superior to that by PLS in both calibration and prediction. When compared with BP artificial neural network, SVR showed less calibration power but more predictive capability. For the dataset of angiotensin-converting enzyme (ACE) inhibitors, the results obtained by support vector machine (SVM) re- gression were equivalent to those by PLS and BP artificial neural network. In both datasets, SVR using linear kernel function behaved well as that using radial basis kernel func- tion. The results showed that there is wide prospect for the application of support vector machine (SVM) into QSAR modeling.