title: Adaptive configuring of radial basis function network by hybrid particle swarm algorithm for QSAR studies of organic compounds.
abstract: The configuring of a radial basis function network (RBFN) consists of selecting the network parameters (centers and widths in RBF units and weights between the hidden and output layers) and network architecture. The issues of suboptimum and overfitting, however, often occur in RBFN configuring. This paper presented a hybrid particle swarm optimization (HPSO) algorithm to simultaneously search the optimal network structure and parameters involved in the RBFN (HPSORBFN) with an ellipsoidal Gaussian function as a basis function. The continuous version of PSO was used for parameter training, while the modified discrete PSO was employed to determine the appropriate network topology. The proposed HPSORBFN algorithm was applied to modeling the inhibitory activities of substituted bis[(acridine-4-carboxamide)propyl]methylamines to murine P388 leukemia cells and the bioactivities of COX-2 inhibitors. The results were compared with those obtained from RBFNs with the parameters optimized by continuous PSO and by conventionally RBFN training the algorithm for a fixed network topology, indicating that the HPSO was competent for RBFN configuring in that it converged quickly toward the optimal solution and avoided overfitting.
authors.affiliation: State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, P. R. 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; D000818:Animals; D045744:Cell Line, Tumor; D002626:Chemistry, Pharmaceutical / Q000379:methods*; D051546:Cyclooxygenase 2 / Q000096:biosynthesis; D016861:Cyclooxygenase Inhibitors / Q000737:chemistry / Q000494:pharmacology; D008744:Methylamines / Q000737:chemistry; D051379:Mice; D008956:Models, Chemical; D015233:Models, Statistical; D016571:Neural Networks, Computer; D016011:Normal Distribution; D009930:Organic Chemicals / Q000737:chemistry*; D010363:Pattern Recognition, Automated; D021281:Quantitative Structure-Activity Relationship; D013678:Technology, Pharmaceutical / Q000379:methods
chemical_list: D016861:Cyclooxygenase Inhibitors; D008744:Methylamines; D009930:Organic Chemicals; D051546:Cyclooxygenase 2