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• W2002909574 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)" @default.
• W2002909574 created "2016-06-24" @default.
• W2002909574 creator A5055862120 @default.
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• W2002909574 date "2009-10-01" @default.
• W2002909574 modified "2023-10-16" @default.
• W2002909574 title "QSAR Modeling for Quinoxaline Derivatives using Genetic Algorithm and Simulated Annealing Based Feature Selection" @default.
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• W2002909574 doi "https://doi.org/10.2174/092986709789352303" @default.
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