Computational Approaches for the Prediction of Antidiabetic Analogues : Validated QSAR models using structural, topological,Estate Numbers, alignment independent descriptors

Diabetes is the most common form of diabetes, accounting for over 90% of cases. Current treatment approaches for diabetes include diet, exercise, and a variety of pharmacologic agents, including insulin. The work is an attempt to generate predictive QSAR models based on QSAR method and to find the structural features of dipeptidyl peptidase IV inhibitors to guide the rational synthesis of activity. The developed models provide insight into the influence of various interactive fields on the activity and, thus, can help in designing and forecasting the dipeptidyl peptidase IV inhibitors. In each series, significant correlations are found between the inhibition potencies of specific dipeptidyl peptidase IV inhibitors and some physicochemical and lipophilicity, hydrophobic parameter of the compounds explained by different regression equations. Our results contribute to the better understanding of the mechanism of biological activity antidiabetic drug.