Additional file 3 of Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models

Additional file 3: Table S1. The performance of the top three runs and the worst three runs among the 50 times independent runs given by the XGBoost model for the 11 datasets. Table S2. The performance comparison (MAE metric) of the four descriptor-based and four graph-based models on the three regression datasets. Table S3. The performance comparison (R2 metric) of the four descriptor-based and four graph-based models on the three regression datasets. Table S4: The performance comparison (average RMSE) of the 50 times independent runs on three regression datasets including ESOL, FreeSolv, and Lipop before/after removing the top three related descriptors given by the four descriptor-based models (SVM, XGBoost, RF and DNN). All the models named with suffix ‘1’ refer to the models developed based on the remaining descriptors. Table S5. The detailed information for the 11 washed datasets. Table S6. The performance comparison of the 50 times independent runs on four datasets including BBBP, Tox21, ToxCast, and SIDER before/after washing for the XGBoost and Attentive FP models.