A Quantitative Structural Relationship study had been performed on Alkyl Alkoxy Pyrazole [1, 5-c] quinazoline-2-carboxylate by using multiple linear regression to identify descriptors, which are actually focusing towards the biological activity. The best predictive QSAR model derived and validated in order to Glutamate receptor models by using a combination of different physicochemical parameters such as steric, electronic and topological. The final QSAR model shows a good predictivity and statistical validation respectively as a NMDA receptor final model had Correlation Coefficient r = 0.928, Square Correlation Coefficient r2 = 0.806, Cross validation Coefficient Q2LOO = 0.685, adjusted correlation coefficient R2adj =0.821, predicted root mean squared error RMSEpred =0.352, predictive residual sum of square S press = 0.352 and standard deviation s= 0.31. On the other hand, the built AMPA generated model had Correlation Coefficient r = 0.927, Square Correlation Coefficient R2 = 0.859, Cross validation Coefficient Q2LOO =0.747, adjusted correlation coefficient R2adj = 0.823, Predicted root mean squared error RMSE pred =0.344, Predictive residual sum of squares Spress =0.465and standard deviation s= 0.30. It was observed that the glutamate receptor (NMDA/AMPA) had a lipophilic, steric volume and electron withdrawing descriptors were crucial in imparting higher potency to NMDA Glycine and AMPA receptor.
The obtained result reveals the good predicting the inhibitory potential of the NMDA/AMPA conjugates of new molecules with more accuracy.
Select your language of interest to view the total content in your interested language