title: Adaptive variable-weighted support vector machine as optimized by particle swarm optimization algorithm with application of QSAR studies.
abstract: Representing a compound by a numerous structural descriptors becomes common in quantitative structure-activity relationship (QSAR) studies. As every descriptor carries molecular structure information more or less, it seems more advisable to investigate all the possible descriptor vectors rather than traditional variable selection when building a QSAR model. Based on particle swarm optimization (PSO) algorithm, a more flexible descriptor selection and model construction method variable-weighted support vector machine (VW-SVM) is proposed. The new strategy adopted in this paper is to weight all structural descriptors with continuous non-negative values rather than removing or reserving any ones arbitrarily. The manner of invoking PSO to seek non-negative weights of variables can be regarded as a process of searching optimized rescaling for every molecular structural descriptor. Moreover, PSO is employed to search the optimal parameters of VW-SVM model besides variable weights, enables the construction of a rational and adaptive parameter-free QSAR model according to the performance of the total model. Results obtained by investigating glycogen synthase kinase-3α inhibitors and carbonic anhydrase II inhibitors indicate VW-SVM can hold more useful structure information of compounds than other methods as optimally weighting all the descriptors, consequently leading to precisely QSAR models coupled with developed performance both in training and in prediction.
authors.affiliation: State Key Laboratory of Chemo/Biosensing and Chemometrics, Laboratory of Tobacco Chemistry, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, PR China.
authors.forename: Jian-Hui
authors.initials: JH
authors.lastname: Wen
authors.forename: Ke-Jun
authors.initials: KJ
authors.lastname: Zhong
authors.forename: Li-Juan
authors.initials: LJ
authors.lastname: Tang
authors.forename: Jian-Hui
authors.initials: JH
authors.lastname: Jiang
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; D000818:Animals; D001185:Artificial Intelligence; D024402:Carbonic Anhydrase II / Q000037:antagonists & inhibitors; D002257:Carbonic Anhydrase Inhibitors / Q000737:chemistry / Q000494:pharmacology; D038362:Glycogen Synthase Kinase 3 / Q000037:antagonists & inhibitors; D006801:Humans; D008301:Maleimides / Q000737:chemistry / Q000494:pharmacology; D047428:Protein Kinase Inhibitors / Q000737:chemistry / Q000494:pharmacology; D021281:Quantitative Structure-Activity Relationship; D013449:Sulfonamides / Q000737:chemistry / Q000494:pharmacology
chemical_list: D002257:Carbonic Anhydrase Inhibitors; D008301:Maleimides; D047428:Protein Kinase Inhibitors; D013449:Sulfonamides; C028525:N-phenylmaleimide; D038362:Glycogen Synthase Kinase 3; C448021:glycogen synthase kinase 3 alpha; D024402:Carbonic Anhydrase II