Studies on QSAR models for the anti-virus effect of oseltamivir derivatives targeting H5N1 based on Mix-Kernel support vector machine

Abstract

To assist in design and synthesis of novel oseltamivir derivatives as to help yield more inhibitors with lower time and cost, four quantitative structure-activity relationship (QSAR) models on 83 ODs with methods of random forest (RF), extreme gradient boosting (XGBoost), radial basis kernel function support vector machine (RBF-SVM) and mix kernel function support vector machine (MIX-SVM) were established. In the study, five descriptors of the highest importance were identified by RF. To further examine the robustness and stability of the four models, leave-one-out cross validation was adapted to the models and R, Rwere used to measure the performance. The model built by MIX-SVM performed best on the dataset: R² on training set and test set were 0.963 and 0.961, mean squared error (MSE) on training set and test set were 0.055 and 0.054, and R, R were 0.918 and 0.889, respectively. Furthermore, five important descriptors were selected for analysis, and the modelling method with the application of MIX-SVM achieved great prediction performance, which could facilitate the design and synthesis of oseltamivir derivatives.