Molecular Modeling, Docking, and QSAR Studies on A Series of N-arylsulfonyl-N-2-pyridinyl-piperazines Analogs Acting as Anti-Diabetic Agents

Abstract

The Molecular structure of compounds contains a lot of information that can be used further. A classical Quantitative Structure Activity Relationship (QSAR) method was used to decode that information based on the descriptors. The study was performed on a mono-substituted series of Glucokinase-Glucokinase regulatory protein inhibitors (GK-GKRP/GCKR). A sequential application of the statistical method, both linear and nonlinear, has been used in the study which includes Multiple Linear Regression (MLR), Partial Least Square (PLS), and Artificial Neural Network (ANN). The developed model was validated using various statistical methods to evidently prove its reliability and precision. This knowledge will be used to design a new compound. Docking studies will be performed to establish the binding pattern of the designed compound. The prophetic power and robustness of the model containing 26 compounds in the training set were proven by the various statistical parameter s value: 0.30, F-value: 41.8, r: 0.94, r2: 0.88, r2CV: 0.77. The model gives insight into the various descriptors that are selected for the present study. The present study not only shows the contribution of various substituents in the biological activity but also indicates the changes that can be done to design the new potent molecules with more selectivity and less toxicity.