train from scratch on molecule datasets

Hello, I am trying to use Graphormer on other commonly used datasets from MoleculeNet (https://moleculenet.org/datasets-1) to check the performance, such as BACE, BBBP, etc. I have used the default hparams in the script of molhiv, but the results are horrible...

1. May I know have you tried your model on these datasets without pretrained model? And do you have any suggestions on the hparams for these datasets if we want to train from scratch? I am trying to find out why the results are so bad...
2. For molhiv without pretrained model, I have tried with the provided script in the examples folder, with not adding the "checkpoint_path" argument, and train for 100 epochs. But the best val score is only around 0.763 and the corresponding test score is only 0.636... I don't know what goes wrong... May I know have you tried to use Graphormer directly on molhiv without pretrained model? How is the performance?
   Thank you.

Start with the provided script in the examples folder and reproduce molhiv training without the checkpoint_path argument, using the stated 100 epochs and default hyperparameters. Compare the validation and test scores, then investigate whether the same behavior occurs on the mentioned MoleculeNet datasets. Done means identifying the cause of the low scores or documenting confirmed from-scratch results and suitable settings.