Support vector machine based training of multilayer feedforward neural networks as optimized by particle swarm algorithm: application in QSAR studies of bioactivity of organic compounds.
Multilayer feedforward neural networks (MLFNNs) are important modeling techniques widely used in QSAR studies for their ability to represent nonlinear relationships between descriptors and activity. However, the problems of overfitting and premature convergence to local optima still pose great challenges in the practice of MLFNNs. To circumvent these problems, a support vector machine (SVM) based training algorithm for MLFNNs has been developed with the incorporation of particle swarm optimization (PSO). The introduction of the SVM based training mechanism imparts the developed algorithm with inherent capacity for combating the overfitting problem. Moreover, with the implementation of PSO for searching the optimal network weights, the SVM based learning algorithm shows relatively high efficiency in converging to the optima. The proposed algorithm has been evaluated using the Hansch data set. Application to QSAR studies of the activity of COX-2 inhibitors is also demonstrated. The results reveal that this technique provides superior performance to backpropagation (BP) and PSO training neural networks.
Lin
Wei-Qi
WQ
State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
Jiang
Jian-Hui
JH
Zhou
Yan-Ping
YP
Wu
Hai-Long
HL
Shen
Guo-Li
GL
Yu
Ru-Qin
RQ
D000465:Algorithms; D003198:Computer Simulation; D052246:Cyclooxygenase 2 Inhibitors / Q000737:chemistry*; D007093:Imidazoles / Q000737:chemistry*; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship
D052246:Cyclooxygenase 2 Inhibitors; D007093:Imidazoles