QSAR Model Based Gradient Boosting Regression of N-Arylsulfonyl-Indole-2-Carboxamide Derivatives as Inhibitors for Fructose-1,6-Bisphosphatase

Abstract

As is known to all, diabetes metellius is a global health threaten and it has caused worldwide attention of scientists. To get a better investigation of the drug design of diabetes, we used heuristic method to established the linear model and used Gradient Boosting Regression to establish the nonlinear model of Fructose-1,6-Bisphosphatse inhibitor successively. In this study, 84 derivatives of N-Arylsulfonyl-Indole-2-Carboxamide were introduced into the models, two outstanding QSAR models with 2 molecule descriptors were established successfully. Grandient Boosting Regression rendered a good correlation with R2 of 0.943 and MSE of 0.135 for the training set, 0.916 and 0.213 for test set, which also proves the feasibility of the implementation of the new method GBR in the field of QSAR. Meanwhile, the optimal model displayed wonderful statistical significance. This study shows unlimited potential for design of new drugs for diabetes.