A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining

ICML 2023

Shengchao Liu⁺, Weitao Du⁺, Zhiming Ma, Hongyu Guo, Jian Tang

⁺ Equal contribution

[Project Page] [Paper] [ArXiv] [Checkpoints on HuggingFace]

MoleculeSDE is GraphMVPv2, follow-up of GraphMVP
It includes two components:
- Contrastive learning
- Generative learning:
  - One 2D->3D diffusion model. Frame-based SE(3)-equivariant and reflection anti-symmetric model
  - One 3D->2D diffusion model. SE(3)-invariant.

All the pretrained checkpoints are available on this HuggingFace link. You can find detailed mapping between checkpoints and tables in file README_checkpoints.md.

Environments

conda create -n Geom3D python=3.7
conda activate Geom3D
conda install -y -c rdkit rdkit
conda install -y numpy networkx scikit-learn
conda install -y -c conda-forge -c pytorch pytorch=1.9.1
conda install -y -c pyg -c conda-forge pyg=2.0.2
pip install ogb==1.2.1

pip install sympy

pip install ase  # for SchNet

pip intall -e .

Datasets

For PCQM4Mv2 (pretraining) dataset

Download the dataset from PCQM4Mv2 website under folder data/PCQM4Mv2/raw:

.
├── data
│   └── PCQM4Mv2
│       └── raw
│           ├── data.csv
│           ├── data.csv.gz
│           ├── pcqm4m-v2-train.sdf
│           └── pcqm4m-v2-train.sdf.tar.gz

Then run examples/generate_PCQM4Mv2.py.

For QM9, it is automatically downloaded in pyg class. The default path is data/molecule_datasets/QM9.
For MD17, it is automatically downloaded in pyg class. The default path is data/MD17.

For MoleculeNet, please follow GraphMVP instructions. The dataset structure is:

.
├── data
│   ├── molecule_datasets
│   │   ├── bace
│   │   │   ├── BACE_README
│   │   │   └── raw
│   │   │       └── bace.csv
│   │   ├── bbbp
...............

Pretraining

A quick demo on pretraining is:

cd examples

python pretrain_MoleculeSDE.py \
--verbose --input_data_dir=../data --dataset=PCQM4Mv2 \
--model_3d=SchNet \
--lr=1e-4 --epochs=50 --num_workers=0 --batch_size=256 --SSL_masking_ratio=0 --gnn_3d_lr_scale=0.1 --dropout_ratio=0 --graph_pooling=mean --emb_dim=300 --epochs=1 \
--SDE_coeff_contrastive=1 --CL_similarity_metric=EBM_node_dot_prod --T=0.1 --normalize --SDE_coeff_contrastive_skip_epochs=0 \
--SDE_coeff_generative_2Dto3D=1 --SDE_2Dto3D_model=SDEModel2Dto3D_02 --SDE_type_2Dto3D=VE --use_extend_graph \
--SDE_coeff_generative_3Dto2D=1 --SDE_3Dto2D_model=SDEModel3Dto2D_node_adj_dense --SDE_type_3Dto2D=VE --noise_on_one_hot \
--output_model_dir=[MODEL_DIR]

Notice that the [MODEL_DIR] is where you are going to save your models/checkpoints.

Downstream

The downstream scripts can be found under the examples folder. Below we illustrate few simple examples.

finetune_MoleculeNet.py:

python finetune_MoleculeNet.py \
--dataset=tox21 \
--input_model_file=[MODEL_DIR]/model_complete.pth

finetune_QM9.py:

python finetune_QM9.py \
--dataset=QM9 --task=gap \
--model_3d=SchNet \
--input_model_file=[MODEL_DIR]/model_complete.pth

finetune_MD17.py:

python finetune_MD17.py \
--dataset=MD17 --task=aspirin \
--model_3d=SchNet \
--input_model_file=[MODEL_DIR]/model_complete.pth

Cite Us

Feel free to cite this work if you find it useful to you!

@inproceedings{liu2023group,
  title={A group symmetric stochastic differential equation model for molecule multi-modal pretraining},
  author={Liu, Shengchao and Du, Weitao and Ma, Zhi-Ming and Guo, Hongyu and Tang, Jian},
  booktitle={International Conference on Machine Learning},
  pages={21497--21526},
  year={2023},
  organization={PMLR}
}

chao1224 / MoleculeSDE

readme