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Application of genetic algorithm-kernel partial least square as a novel nonlinear feature selection method: Activity of carbonic anhydrase II inhibitors

Jalali Heravi, M ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1016/j.ejmech.2006.12.020
  3. Publisher: 2007
  4. Abstract:
  5. This paper introduces the genetic algorithm-kernel partial least square (GA-KPLS), as a novel nonlinear feature selection method. This technique combines genetic algorithms (GAs) as powerful optimization methods with KPLS as a robust nonlinear statistical method for variable selection. This feature selection method is combined with artificial neural network to develop a nonlinear QSAR model for predicting activities of a series of substituted aromatic sulfonamides as carbonic anhydrase II (CA II) inhibitors. Eight simple one- and two-dimensional descriptors were selected by GA-KPLS and considered as inputs for developing artificial neural networks (ANNs). These parameters represent the role of acceptor-donor pair, hydrogen bonding, hydrosolubility and lipophilicity of the active sites and also the size of the inhibitors on inhibitor-isozyme interaction. The accuracy of 8-4-1 networks was illustrated by validation techniques of leave-one-out (LOO) and leave-multiple-out (LMO) cross-validations and Y-randomization. Superiority of this method (GA-KPLS-ANN) over the linear one (MLR) in a previous work and also the GA-PLS-ANN in which a linear feature selection method has been used indicates that the GA-KPLS approach is a powerful method for the variable selection in nonlinear systems. © 2007 Elsevier Masson SAS. All rights reserved
  6. Keywords:
  7. Aromatic compound ; Carbonate dehydratase II ; Carbonate dehydratase inhibitor ; Sulfonamide ; Algorithm ; Artificial neural network ; Biological activity ; Drug activity ; Drug structure ; Enzyme inhibition ; Hydrogen bond ; Kernel method ; Lipophilicity ; Mathematical computing ; Nonlinear system ; Regression analysis ; Solubility ; Statistical analysis ; Substitution reaction ; Technique ; Algorithms ; Carbonic Anhydrase II ; Carbonic Anhydrase Inhibitors ; Hydrogen Bonding ; Least-Squares Analysis ; Neural Networks (Computer) ; Quantitative Structure-Activity Relationship ; Solubility
  8. Source: European Journal of Medicinal Chemistry ; Volume 42, Issue 5 , 2007 , Pages 649-659 ; 02235234 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0223523407000074