Particle swarm optimization and neural network application for QSAR

Summary form only given. A successful approach to building QSAR models was proposed by other researchers. It uses binary particle swarm optimization (BPSO) for feature selection in the first stage, and a back propagation neural network in the second stage to generate a QSAR model based on the features selected in the first stage. We start by reestablishing the results of this approach on an extended number of data sets. A new method is then proposed that addresses the limitation of back propagation. This approach uses particle swarm optimization (PSO) in the second stage for training and bootstrap aggregation (bagging) in order to overcome the instability of PSO. The proposed approach yields robust QSAR models, while reducing the variability due to the choice of the back propagation parameters.

[1]  W. Dunn,et al.  Principal components analysis and partial least squares regression , 1989 .

[2]  Donald R. Scott,et al.  UNIPALS: Software for principal components analysis and partial least squares regression , 1989 .

[3]  D. Livingstone,et al.  Structure-activity relationships of antifilarial antimycin analogues: a multivariate pattern recognition study. , 1990, Journal of medicinal chemistry.

[4]  T. A. Andrea,et al.  Applications of neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors. , 1991, Journal of medicinal chemistry.

[5]  R. Boggia,et al.  Genetic algorithms as a strategy for feature selection , 1992 .

[6]  James H. Wikel,et al.  The use of neural networks for variable selection in QSAR , 1993 .

[7]  James W. McFarland,et al.  On Identifying Likely Determinants of Biological Activity in High Dimensional QSAR Problems , 1994 .

[8]  H. Kubinyi.  Variable Selection in QSAR Studies. II. A Highly Efficient Combination of Systematic Search and Evolution , 1994 .

[9]  Anton J. Hopfinger,et al.  Application of Genetic Function Approximation to Quantitative Structure-Activity Relationships and Quantitative Structure-Property Relationships , 1994, J. Chem. Inf. Comput. Sci..

[10]  D. Maddalena,et al.  Prediction of receptor properties and binding affinity of ligands to benzodiazepine/GABAA receptors using artificial neural networks. , 1995, Journal of medicinal chemistry.

[11]  Chris L. Waller,et al.  Development and Validation of a Novel Variable Selection Technique with Application to Multidimensional Quantitative Structure—Activity Relationship Studies. , 1999 .

[12]  Chris L. Waller,et al.  Development and Validation of a Novel Variable Selection Technique with Application to Multidimensional Quantitative Structure-Activity Relationship Studies , 1999, J. Chem. Inf. Comput. Sci..

[13]  Kristin P. Bennett,et al.  Bagging neural network sensitivity analysis for feature reduction for in-silico drug design , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[14]  D. Agrafiotis,et al.  Feature selection for structure-activity correlation using binary particle swarms. , 2002, Journal of medicinal chemistry.

[15]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.