We used the following packages under Python 3.7.
pytorch 1.13.1
torch-cluster 1.6.1
torch-geometric 2.6.0
torch-scatter 2.1.1
torch-sparse 0.6.17
rdkit 2022.9.5
Please run pretraining.py for downstream adaptations.
The pre-trained models we use follow the training steps of the paper Strategies for Pre-training Graph Neural Networks and GraphMAE
The pre-training and downstream datasets used in our experiments are referred to the paper Strategies for Pre-training Graph Neural Networks. You can download the biology and chemistry datasets from their repository.
- To run the codes successfully, the downloaded datasets should be placed in
/dataset_confand/dataset_infofor pre-training
(If you're using 3D-level pretext task, you'll need to use the /dataset_conf)
(If you are not using 3D-level pretext task, you'll need to use the /dataset_info)
- To run the codes successfully, the downloaded datasets should be placed in
/datasetfor fine-tuning
We use Pretrain/dataset_conf/zinc_2m_MD and Pretrain/dataset_info/zinc_2m_MD
(Preprocessed data from zinc_standard_agent dataset, you can get here)
Please run finetune.py for downstream adaptations.
We provide pretrained MORE (Finetune/pretrain/MORE.pth)
For pretraining, Pretrain/example.ipynb
For Fine-tuning, Finetune/example.ipynb