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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

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QSAR modeling for quinoxaline derivatives using genetic algorithm and simulated annealing based feature selection.

P Ghosh¹, M C Bagchi

¹Structural Biology and Bioinformatics Division, Indian Institute of Chemical Biology, 4 Raja S.C. Mullick Road, Jadavpur, Kolkata-700032, India.

Current Medicinal Chemistry

|September 15, 2009

Summary

Quantitative Structure-Activity Relationship (QSAR) models were developed to predict anti-tubercular activity in quinoxaline derivatives. Simulated annealing proved effective for variable selection in 3D-QSAR modeling, aiding rational drug design.

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Area of Science:

Medicinal Chemistry
Computational Chemistry
Pharmacology

Background:

Quinoxaline derivatives show potential as anti-tubercular agents.
Rational drug design requires predictive models of biological activity.
Effective Quantitative Structure-Activity Relationship (QSAR) models depend on selecting relevant molecular descriptors.

Purpose of the Study:

To develop and compare 2D and 3D-QSAR models for predicting anti-tubercular activity of quinoxaline derivatives.
To investigate the efficacy of genetic algorithm (GA) and simulated annealing (SA) for variable selection in QSAR model development.
To compare the performance of linear and non-linear QSAR approaches.

Main Methods:

Development of 2D-QSAR models using GA-Partial Least Squares (GA-PLS) and SA-Partial Least Squares (SA-PLS).
Application of Kohonen network and counter propagation artificial neural network (CP-ANN) with GA and SA feature selection.
Utilizing 3D-QSAR techniques to analyze steric and electrostatic contributions.
Validation of models on training and test sets.

Main Results:

Topological and electrostatic descriptors were identified as key factors for anti-tubercular activity in 2D-QSAR models.
Both linear (PLS) and non-linear (ANN) approaches were investigated for predictive modeling.
3D-QSAR analysis identified two effective models using GA-PLS and SA-PLS for predicting anti-tubercular activity.
Simulated annealing demonstrated high effectiveness as a variable selection method for 3D-QSAR.

Conclusions:

QSAR modeling, particularly 3D-QSAR with simulated annealing for variable selection, is a valuable tool for designing selective quinoxaline derivatives with anti-tubercular activity.
The study highlights the importance of descriptor selection in building robust predictive QSAR models.
SA-PLS and GA-PLS methods provide effective frameworks for predicting anti-tubercular activity, with SA showing particular promise.