title: Boosting support vector regression in QSAR studies of bioactivities of chemical compounds.
abstract: 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.
authors.affiliation: State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, PR China.
authors.forename: Yan-Ping
authors.initials: YP
authors.lastname: Zhou
authors.forename: Jian-Hui
authors.initials: JH
authors.lastname: Jiang
authors.forename: Wei-Qi
authors.initials: WQ
authors.lastname: Lin
authors.forename: Hong-Yan
authors.initials: HY
authors.lastname: Zou
authors.forename: Hai-Long
authors.initials: HL
authors.lastname: Wu
authors.forename: Guo-Li
authors.initials: GL
authors.lastname: Shen
authors.forename: Ru-Qin
authors.initials: RQ
authors.lastname: Yu
mesh_terms: 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
chemical_list: D004791:Enzyme Inhibitors; D016859:Lipoxygenase Inhibitors; D009578:Nitrobenzenes; D014227:Triazines