QSAR study on 5-lipoxygenase inhibitors based on support vector machine.
QSAR study on a data set of 5-lipoxygenase inhibitors (1-phenyl [2H]-tetrahydro-triazine-3-one analogues) was carried out by using Support Vector Regression (SVR) and physicochemical parameters. Wrapper methods were used to select descriptors, while Leave-One-Out Cross Validation (LOOCV) method and independent set test were used to judge the predictive power of different models. We found out that the generalization ability of SVR model outperformed multiple linear regression (MLR) and Partial Least Squares (PLS) models in this work. An online web server for activity prediction is available at http://chemdata.shu.edu.cn/qsar5lip.
College of Life Science, Shanghai University, Shanghai 200444, China. bingniu@shu.edu.cn
Bing
B
Niu
Qiang
Q
Su
Xiaochen
X
Yuan
Wencong
W
Lu
Juan
J
Ding
D001094:Arachidonate 5-Lipoxygenase / Q000378:metabolism*; D020128:Inhibitory Concentration 50; D016018:Least-Squares Analysis; D016014:Linear Models; D016859:Lipoxygenase Inhibitors / Q000737:chemistry* / Q000494:pharmacology*; D021281:Quantitative Structure-Activity Relationship; D060388:Support Vector Machine
D016859:Lipoxygenase Inhibitors; D001094:Arachidonate 5-Lipoxygenase