Structural analysis of structurally diverse α-glucosidase inhibitors for active site feature analysis.

In the present investigation, a QSAR analysis on structurally diverse α-glucosidase inhibitors (andrographolide, chromenone, triazole derivatives) was performed and the developed models were validated by various validation methods (LMO, LOO, LSO, bootstrapping, Y-randomization and test set). The statistical parameters calculated for the models show that the developed models are statistically significant and have predicted the activities with small residual errors. The crossvalidated correlation coefficient (Q(2)) values obtained from different validation methods show >0.7 for both the models. Other correlations coefficient statistical parameters (R(2)(pred) and R(2)(m)) show that the developed models are reliable and robust. The leave-series-out (LSO) results reveal that the developed models can predict the activity of new compounds and its crossvalidated correlation coefficients' values are comparable with the Q(2) values obtained from other validation methods. The descriptors contributed in the selected models are suggested that the lower/reduced polarizability on the vdW surface area of the molecules and the presence of flexible bonds allow the substituents/side chains in the molecules with free movement and with lesser stretching energy which are favourable for the α-glucosidase inhibitory activity. These results reveal that the developed models are statistically significant and can be used with other molecular modelling works for designing novel α-glucosidase inhibitors with multiple activities (HIV, diabetics, cancer, etc).