Boosting support vector regression in QSAR studies of bioactivities of chemical compounds.
In this paper, boosting has been coupled with SVR to develop a new method, boosting support vector regression (BSVR). BSVR is implemented by firstly constructing a series of SVR models on the various weighted versions of the original training set and then combining the predictions from the constructed SVR models to obtain integrative results by weighted median. The proposed BSVR algorithm has been used to predict toxicities of nitrobenzenes and inhibitory potency of 1-phenyl[2H]-tetrahydro-triazine-3-one analogues as inhibitors of 5-lipoxygenase. As comparisons to this method, the multiple linear regression (MLR) and conventional support vector regression (SVR) have also been investigated. Experimental results have shown that the introduction of boosting drastically enhances the generalization performance of individual SVR model and BSVR is a well-performing technique in QSAR studies superior to multiple linear regression.
Zhou
Yan-Ping
YP
State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, PR China.
Jiang
Jian-Hui
JH
Lin
Wei-Qi
WQ
Zou
Hong-Yan
HY
Wu
Hai-Long
HL
Shen
Guo-Li
GL
Yu
Ru-Qin
RQ
D000465:Algorithms; D003198:Computer Simulation; D004791:Enzyme Inhibitors / Q000737:chemistry* / Q000494:pharmacology; D016014:Linear Models; D016859:Lipoxygenase Inhibitors; D008958:Models, Molecular; D015394:Molecular Structure; D009578:Nitrobenzenes / Q000737:chemistry* / Q000633:toxicity; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis; D014227:Triazines / Q000737:chemistry* / Q000494:pharmacology
D004791:Enzyme Inhibitors; D016859:Lipoxygenase Inhibitors; D009578:Nitrobenzenes; D014227:Triazines