GA Strategy for Variable Selection in QSAR Studies: GA-Based PLS Analysis of Calcium Channel Antagonists

The GAPLS (GA based PLS) program has been developed for variable selection in QSAR studies. The modified GA was employed to obtain a PLS model with high internal predictivity using a small number of variables. In order to show the performance of GAPLS for variable selection, the program was applied to the inhibitor activity of calcium channel antagonists. As a result, variables largely contributing to the inhibitory activity could be selected, and the structural requirements for the inhibitory activity could be estimated in an effective manner.

[1]  Shin-ichi Sasaki,et al.  Multivariate Free-Wilson analysis of α-chymotrypsin inhibitors using PLS , 1996 .

[2]  Han van de Waterbeemd,et al.  Chemometric Methods in Molecular Design: van de Waterbeemd/Chemometric , 1995 .

[3]  Shin-ichi Sasaki,et al.  FREE-WILSON DISCRIMINANT ANALYSIS OF ANTIARRHYTHMIC PHENYLPYRIDINES USING PLS , 1995 .

[4]  C. B. Lucasius,et al.  Understanding and using genetic algorithms Part 2. Representation, configuration and hybridization , 1994 .

[5]  Paul Geladi,et al.  Interactive variable selection (IVS) for PLS. Part 1: Theory and algorithms , 1994 .

[6]  Y. Takahata,et al.  Quantitative Structure-Activity Relationships for 1,4-Dihydropyridine Calcium Channel Antagonists (Nifedipine Analogues): A Quantum ChemicalKlassical Approach , 1994 .

[7]  Hiroshi Yoshida,et al.  Chemometric QSAR studies of antifungal azoxy compounds , 1994, J. Comput. Aided Mol. Des..

[8]  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..

[9]  Hxugo Kubiny Variable Selection in QSAR Studies. I. An Evolutionary Algorithm , 1994 .

[10]  R. Leardi Application of a genetic algorithm to feature selection under full validation conditions and to outlier detection , 1994 .

[11]  Y. Takahata,et al.  Quantitative structure-activity relationships for 1,4-dihydropyridine calcium channel antagonists (nifedipine analogues): a quantum chemical/classical approach. , 1994, Journal of pharmaceutical sciences.

[12]  C. B. Lucasius,et al.  Understanding and using genetic algorithms Part 1. Concepts, properties and context , 1993 .

[13]  Jan van der Greef,et al.  Pyrolysis—mass spectrometry under soft ionization conditions , 1993 .

[14]  John H. Kalivas,et al.  Comparison of Forward Selection, Backward Elimination, and Generalized Simulated Annealing for Variable Selection , 1993 .

[15]  Shin-ichi Sasaki,et al.  Chemical pattern recognition and multivariate analysis for QSAR studies , 1993 .

[16]  G. Cruciani,et al.  Generating Optimal Linear PLS Estimations (GOLPE): An Advanced Chemometric Tool for Handling 3D‐QSAR Problems , 1993 .

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

[18]  T. Liljefors,et al.  Structure-Activity Relationships for Unsaturated Dialdehydes 2. A PLS Correlation of Theoretical Descriptors for Six Compounds with Mutagenic Activity in the Ames Salmonella Assay , 1988 .

[19]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .