For a fair comparison, we utilized the same 5-fold cross-validation and division method for training all compared models in our own hands. So, for each task, we trained model five times and the prediction on test set was the average value from five models. The scaffold split method recommended by Yang et al. was employed to split the datasets into training, validation, and test, with a 8/1/1 ratio . The same criteria adoped by previous works were used to evaluate result: RMSE for regression tasks and ROC-AUC for classification tasks.