Abstract
With a view to the rational design of selective quinoxaline derivatives, 2D and 3D-QSAR models have been developed for the prediction of anti-tubercular activities. Successful implementation of a predictive QSAR model largely depends on the selection of a preferred set of molecular descriptors that can signify the chemico – biological interaction. Genetic algorithm (GA) and simulated annealing (SA) are applied as variable selection methods for model development. 2D-QSAR modeling using GA or SA based partial least squares (GA-PLS and SA-PLS) methods identified some important topological and electrostatic descriptors as important factor for tubercular activity. Kohonen network and counter propagation artificial neural network (CP-ANN) considering GA and SA based feature selection methods have been applied for such QSAR modeling of Quinoxaline compounds. Out of a variable pool of 380 molecular descriptors, predictive QSAR models are developed for the training set and validated on the test set compounds and a comparative study of the relative effectiveness of linear and non-linear approaches has been investigated. Further analysis using 3D-QSAR technique identifies two models obtained by GA-PLS and SA-PLS methods leading to anti-tubercular activity prediction. The influences of steric and electrostatic field effects generated by the contribution plots are discussed. The results indicate that SA is a very effective variable selection approach for such 3D-QSAR modeling.
Keywords: Quinoxaline derivatives, Quantitative Structure Activity Relationship (QSAR), 2D and 3D descriptors, genetic algorithm (GA), simulated annealing (SA), Partial Least Squares (PLS), counter propagation artificial neural network (CP-ANN)
Current Medicinal Chemistry
Title: QSAR Modeling for Quinoxaline Derivatives using Genetic Algorithm and Simulated Annealing Based Feature Selection
Volume: 16 Issue: 30
Author(s): P. Ghosh and M. C. Bagchi
Affiliation:
Keywords: Quinoxaline derivatives, Quantitative Structure Activity Relationship (QSAR), 2D and 3D descriptors, genetic algorithm (GA), simulated annealing (SA), Partial Least Squares (PLS), counter propagation artificial neural network (CP-ANN)
Abstract: With a view to the rational design of selective quinoxaline derivatives, 2D and 3D-QSAR models have been developed for the prediction of anti-tubercular activities. Successful implementation of a predictive QSAR model largely depends on the selection of a preferred set of molecular descriptors that can signify the chemico – biological interaction. Genetic algorithm (GA) and simulated annealing (SA) are applied as variable selection methods for model development. 2D-QSAR modeling using GA or SA based partial least squares (GA-PLS and SA-PLS) methods identified some important topological and electrostatic descriptors as important factor for tubercular activity. Kohonen network and counter propagation artificial neural network (CP-ANN) considering GA and SA based feature selection methods have been applied for such QSAR modeling of Quinoxaline compounds. Out of a variable pool of 380 molecular descriptors, predictive QSAR models are developed for the training set and validated on the test set compounds and a comparative study of the relative effectiveness of linear and non-linear approaches has been investigated. Further analysis using 3D-QSAR technique identifies two models obtained by GA-PLS and SA-PLS methods leading to anti-tubercular activity prediction. The influences of steric and electrostatic field effects generated by the contribution plots are discussed. The results indicate that SA is a very effective variable selection approach for such 3D-QSAR modeling.
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Cite this article as:
Ghosh P. and Bagchi C. M., QSAR Modeling for Quinoxaline Derivatives using Genetic Algorithm and Simulated Annealing Based Feature Selection, Current Medicinal Chemistry 2009; 16 (30) . https://dx.doi.org/10.2174/092986709789352303
DOI https://dx.doi.org/10.2174/092986709789352303 |
Print ISSN 0929-8673 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-533X |
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