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

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.

Automated summary

The study aims to predict the inhibitory effect of amide derivatives on xanthine oxidase (XO) by building several models based on the theory of quantitative structure-activity relationship (QSAR). Linear and non-linear models were constructed using the heuristic method (HM) and XGBoost, respectively. Among the non-linear models, MIX-SVR method achieved the best result by combining different kernel functions.