Quantitative structure-activity relationship study of amide derivatives as xanthine oxidase inhibitors using machine learning

Abstract

The target of the study is to predict the inhibitory effect of amide derivatives on xanthine oxidase (XO) by building several models, which are based on the theory of the quantitative structure-activity relationship (QSAR). The heuristic method (HM) was used to linearly select descriptors and build a linear model. XGBoost was used to non-linearly select descriptors, and radial basis kernel function support vector regression (RBF SVR), polynomial kernel function SVR (poly SVR), linear kernel function SVR (linear SVR), mix-kernel function SVR (MIX SVR), and random forest (RF) were adopted to establish non-linear models, in which the MIX-SVR method gives the best result. The kernel function of MIX SVR has strong abilities of learning and generalization of established models simultaneously, which is because it is a combination of the linear kernel function, the radial basis kernel function, and the polynomial kernel function. In order to test the robustness of the models, leave-one-out cross validation (LOOCV) was adopted. In a training set, $R^2$ = 0.97 and RMSE = 0.01; in a test set, $R^2$ = 0.95, RMSE = 0.01, and $R_{cv}^2$ = 0.96. This result is in line with the experimental expectations, which indicate that the MIX-SVR modeling approach has good applications in the study of amide derivatives.

Keywords: XGBoost; amide derivatives; particle swarm optimization; quantitative structure activity relationship; random forest; support vector regression; xanthine oxidase inhibitor.