Piecewise Hypersphere Modeling by Particle Swarm Optimization in QSAR Studies of Bioactivities of Chemical Compounds

As the structural diversity in a quantitative structure-activity relationship (QSAR) model increases, constructing a good model becomes increasingly difficult, and simply performing variable selection might not be sufficient to improve the model quality to make it practically usable. To combat this difficulty, an approach based on piecewise hypersphere modeling by particle swarm optimization (PHMPSO) is developed in this paper. It treats the linear models describing the sought-for subsets as hyperspheres which have different radii in the data space. According to the attribute of each hypersphere, all compounds in the training set are allocated to hyperspheres to construct submodels, and particle swarm optimization (PSO) is applied to search the optimal hyperspheres for finding satisfactory piecewise linear models. A new objective function is formulated to determine the appropriate piecewise models. The performance is assessed using three QSAR data sets. Experimental results have shown the good performance of this technique in improving the QSAR modeling.

[1]  Kimito Funatsu,et al.  GA Strategy for Variable Selection in QSAR Studies: GA-Based PLS Analysis of Calcium Channel Antagonists , 1997, J. Chem. Inf. Comput. Sci..

[2]  Rajni Garg,et al.  Comparative QSAR Study of Tyrosine Kinase Inhibitors , 2001 .

[3]  Lemont B. Kier,et al.  Electrotopological State Indices for Atom Types: A Novel Combination of Electronic, Topological, and Valence State Information , 1995, J. Chem. Inf. Comput. Sci..

[4]  David Hartsough,et al.  Toward an Optimal Procedure for Variable Selection and QSAR Model Building , 2001, J. Chem. Inf. Comput. Sci..

[5]  Sung Jin Cho,et al.  Genetic Algorithm Guided Selection: Variable Selection and Subset Selection , 2002, J. Chem. Inf. Comput. Sci..

[6]  Jian-Hui Jiang,et al.  Optimized Partition of Minimum Spanning Tree for Piecewise Modeling by Particle Swarm Algorithm. QSAR Studies of Antagonism of Angiotensin II Antagonists , 2004, J. Chem. Inf. Model..

[7]  A. Höskuldsson.  Variable and subset selection in PLS regression , 2001 .

[8]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[9]  L B Kier,et al.  Derivation and significance of valence molecular connectivity. , 1981, Journal of pharmaceutical sciences.

[10]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[11]  Igor V. Tetko,et al.  Neural Network Studies, 2. Variable Selection , 1996, J. Chem. Inf. Comput. Sci..

[12]  R. Eberhart,et al.  Fuzzy adaptive particle swarm optimization , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[13]  Arup K. Ghose,et al.  Atomic physicochemical parameters for three dimensional structure directed quantitative structure-activity relationships. 4. Additional parameters for hydrophobic and dispersive interactions and their application for an automated superposition of certain naturally occurring nucleoside antibiotics , 1989, J. Chem. Inf. Comput. Sci..

[14]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[15]  C Silipo,et al.  Formulation of de novo substituent constants in correlation analysis: inhibition of dihydrofolate reductase by 2,4-diamino-5-(3,4-dichlorophenyl)-6-substituted pyrimidines. , 1975, Journal of pharmaceutical sciences.

[16]  Kimito Funatsu,et al.  GA Strategy for Variable Selection in QSAR Studies: Application of GA-Based Region Selection to a 3D-QSAR Study of Acetylcholinesterase Inhibitors , 1999, J. Chem. Inf. Comput. Sci..

[17]  Alexander Tropsha,et al.  Novel Variable Selection Quantitative Structure-Property Relationship Approach Based on the k-Nearest-Neighbor Principle , 2000, J. Chem. Inf. Comput. Sci..

[18]  Ru-Qin Yu,et al.  Hybridized particle swarm algorithm for adaptive structure training of multilayer feed‐forward neural network: QSAR studies of bioactivity of organic compounds , 2004, J. Comput. Chem..

[19]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[20]  Alexander Golbraikh,et al.  Antitumor agents. 213. Modeling of epipodophyllotoxin derivatives using variable selection k nearest neighbor QSAR method. , 2002, Journal of medicinal chemistry.

[21]  Lemont B. Kier,et al.  The electrotopological state: structure information at the atomic level for molecular graphs , 1991, J. Chem. Inf. Comput. Sci..

[22]  Shu-Shen Liu,et al.  VSMP: A Novel Variable Selection and Modeling Method Based on the Prediction , 2003, J. Chem. Inf. Comput. Sci..

[23]  Hugo Kubinyi,et al.  Evolutionary variable selection in regression and PLS analyses , 1996 .