2D QSAR STUDY OF POTENT GSK 3β INHIBITOR FOR TREATMENT OF TYPE II DIABETES

The best QSAR model were generated with left of adept and significant descriptors like electronic, lipophilic and topological, using multiple linear regression (MLR) and partial least square (PLS), model further explained by using forward feed neural network analysis (FFNN). QSAR is a kind of technique that directly correlates in between chemical structure to their biological activity. The best MLR statistical expressions were evaluated with good predictive and authenticated ability and the values were S =0.367, F =53.06 r =0.910, r² =0.828, r²_(cv)=0.780. The r² (training and test-set) values of MLR, PLS and FFNN are 0.82, 0.71, 0.82, 0.71 and 0. 81, 0.74 respectively, which predicts the soundness of the model. The model reveals that total dipole moment, bond lipole and kappa 3 are prerequisite descriptors for determining further promising GSK-3β antagonist with high and liable potency against target. In addition to QSAR modelling, Lipinski’s rule of five was employed on a series and we found no violation in it, which means 3-aryl- 4-(arylhydrazono) 1H pyrazol-5-ones has enough good pharmacokinetic profile, and it become more accentuated when orally active anti-diabetic agents will formed.