Particle swarm algorithm trained neural network for QSAR studies of inhibitors of platelet-derived growth factor receptor phosphorylation.
The multilayer feed-forward artificial neural network (ANN) has been widely used in QSAR studying. Back-propagation algorithm (BP) and the use of evolutionary search as an ANN training method has some limitations associated with overfitting, local optimum problems and slow convergence rate. In this paper, particle swarm optimization (PSO) as a relatively new optimization technique has been used in ANN training. Compared to ANN trained by BP algorithm and evolutionary search, ANN training by PSO algorithm (PSONN) show satisfactory performance, converges quickly towards the optimal position and can avoid overfitting in some extent. The PSONN has been testified by using in QSAR modeling for inhibitory activity of 4-[4-(N-substituted (thio) carbamoyl)-1-piperazinyl]-6,7-dimethoxyquinazoline derivatives.
Shen
Qi
Q
Chemistry Department, Zhengzhou University, Zhengzhou 450052, China. sheqi@zzu.edu.cn
Shi
Wei-min
WM
Yang
Xi-ping
XP
Ye
Bao-xian
BX
D000465:Algorithms; D016571:Neural Networks, Computer; D010766:Phosphorylation; D021281:Quantitative Structure-Activity Relationship; D017479:Receptors, Platelet-Derived Growth Factor / Q000037:antagonists & inhibitors*
D017479:Receptors, Platelet-Derived Growth Factor