2-Dimensional Quantitative Structure-Activity Relationship Modeling Study of Glycine/ N-methyl-D-aspartate Antagonist Inhibition: Genetic Function Approximation Vis-à-vis Multiple Linear Regression Methods

Nitin S. Sapre1,*, Nilanjana Pancholi1, Swagata Gupta2 and Arun Sikarwar3
1 Department of Applied Chemistry, Shri GS Institute of Technology and Sciences, Indore, MP, India, Pin 452001.
2 Department of Chemistry, Govt. P.G. College, MHOW, MP, India.
3 Department of Chemistry, Holkar Science College, MP, India.
E-mail: sukusap@yahoo.com

A comparative study of genetic function approximation (GFA) and multiple linear regression analysis(MLR) techniques for understanding 2D quantitative structure-activity relationship (2D-QSAR) on N-methyl-D-aspartate (NMDA) inhibitors was conducted using distance and connectivity based topological indices (Wiener, Balaban and Randic Indices). Models generated were used to predict the inhibitory activity for a set of test compounds. The results indicated that the GFA method proved to be superior of the two in developing 2D QSAR model in all the cases (Uni- as well as multi-variate). Individual topological indices have also been studied to understand their correlation potential. In all the cases (Wiener, Balaban and Randic), the results gave a high value of correlation (R2 > 0.80, Q2 > 0.79) for the GFA method while the MLR method yielded poor correlation (R2 < 0.60 and Q2 < 0.55). Among the three indices, Randic connectivity index proved to be the best in describing the 2D-QSAR for this series of NMDA inhibitors (R2 = 0.893, Q2 = 0.880, F-ratio = 216.393)

Keywords: QSAR, NMDA, GFA, MLR, Wiener index, Randic index, Balaban index