Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials

Authors: Shengchao Liu, Weitao Du, Yanjing Li, Zhuoxinran Li, Zhiling Zheng, Chenru Duan, Zhiming Ma, Omar Yaghi, Anima Anandkumar, Christian Borgs, Jennifer Chayes, Hongyu Guo, Jian Tang

[ArXiv]

This is Geom3D, a platfrom for geometric modeling on 3D structures:

<p align="center"> <img src="./figure/pipeline.jpg" /> </p>

Environment

Conda

Setup the anaconda

bashCopy
wget https://repo.continuum.io/archive/Anaconda3-2019.10-Linux-x86_64.sh
bash Anaconda3-2019.10-Linux-x86_64.sh -b
export PATH=$PWD/anaconda3/bin:$PATH

Packages

Start with some basic packages.

bashCopy
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

pip install lie_learn # for TFN and SE3-Trans

pip install packaging # for SEGNN
pip3 install e3nn # for SEGNN

pip install transformers # for smiles
pip install selfies # for selfies

pip install atom3d # for Atom3D
pip install cffi # for Atom3D
pip install biopython # for Atom3D

pip install cython # for pyximport 

conda install -y -c conda-forge py-xgboost-cpu # for XGB

Datasets

We cover three types of datasets:

• Small Molecules
  • QM9
  • MD17
  • rMD17
  • COLL
• Proteins
  • EC
  • FOLD
• Small Molecules and Proteins
  • LBA
  • LEP
• Materials
  • MatBench
  • QMOF

For dataset acquisition:

• We provide a set of raw and processed dataset HuggingFace. You can download the data using python download_data.py under ./data.
• Please refer to the data folder for more details.

Overview of Models

Representation Models

Geom3D includes the following representation models:

• SchNet, NeurIPS'18
• TFN, NeurIPS'18 Workshop
• DimeNet, ICLR'20
• SE(3)-Trans, NeurIPS'20
• EGNN, ICML'21
• PaiNN, ICML'21
• GemNet, NeurIPS'21
• SphereNet, ICLR'22
• SEGNN, ICLR'22
• NequIP, Nature Communications'22
• Allegro, Nature Communications'23
• Equiformer, ICLR'23
• GVP-GNN, ICLR'21
• IEConv, ICLR'21
• GearNet, ICLR'23
• ProNet, ICLR'23
• CDConv, ICLR'23

We also include the following 7 1D models and 11 2D models (specifically for small molecules):

• 1D Fingerprints: MLP, RF, XGB
• 1D SMILES: CNN, BERT
• 1D Selfies: CNN, BERT
• 2D topology:
  • GCN, NeurIPS'2015
  • ENN-S2S, ICML'17
  • GraphSAGE, NeurIPS'17
  • GAT, ICLR'2018
  • GIN, ICLR'2019
  • D-MPNN, ACS-JCIM'2019
  • N-Gram Graph, NeurIPS'2019
  • PNA, NeurIPS'2020
  • Graphormer, NeurIPS'21
  • AWARE, TMLR'2022
  • GraphGPS, NeurIPS'22

Notice that there is no pretraining considered at this stage. For geoemtric pretraining models, please check the following section.

Geometric Pretraining

We include the following 14 geometric pretraining methods:

• Pure 3D:
  • Supervised
  • Atom Type Prediction
  • Distance Prediction
  • Angle Prediction
  • 3D InfoGraph, from GeoSSL, ICLR'23
  • GeoSSL-RR, from GeoSSL, ICLR'23
  • GeoSSL-InfoNCE, from GeoSSL, ICLR'23
  • GeoSSL-EBM-NCE, from GeoSSL, ICLR'23
  • GeoSSL-DDM, ICLR'23
  • GeoSSL-DDM-1L, ICLR'23
  • 3D-EMGP, AAAI'23
• Joint 2D-3D:
  • GraphMVP, ICLR'22
  • 3D InfoMax, ICML'22
  • MoleculeSDE, ICML'23

Scripts

The python scripts can be found in examples_3D. We list the bash scripts (and hyperparameters) in scripts. For example, the bash script for SchNet on QM9 is:

cd examples_3D

export model_3d=SchNet
export dataset=QM9
export task_list=(mu alpha homo lumo gap r2 zpve u0 u298 h298 g298 cv)

export lr_list=(5e-4)
export lr_scheduler_list=(CosineAnnealingLR)
export split=customized_01
export seed=42
export emb_dim_list=(128 300)
export batch_size_list=(128)

export epochs=1000

for task in "${task_list[@]}"; do
for lr in "${lr_list[@]}"; do
for lr_scheduler in "${lr_scheduler_list[@]}"; do
for emb_dim in "${emb_dim_list[@]}"; do
for batch_size in "${batch_size_list[@]}"; do

    export output_model_dir=output/random/"$model_3d"/"$dataset"/"$task"_"$split"_"$seed"/"$lr"_"$lr_scheduler"_"$emb_dim"_"$batch_size"_"$epochs"
    export output_file="$output_model_dir"/result.out
    mkdir -p "$output_model_dir"

    python finetune_QM9.py \
    --model_3d="$model_3d" --dataset="$dataset" --epochs="$epochs" \
    --task="$task" \
    --split="$split" --seed="$seed" \
    --batch_size="$batch_size" \
    --emb_dim="$emb_dim" \
    --lr="$lr" --lr_scheduler="$lr_scheduler" --no_eval_train --print_every_epoch=1 --num_workers=8 \
    --output_model_dir="$output_model_dir" \
    > "$output_file"
    
done
done
done
done
done

Now only the bash scripts for QM9 are available. We will release the complete version soon, together with Notebook demo. Please stay tuned.

Checkpoints

Checkpoints for all the pretraining and downstream tasks will be released soon.

Cite us

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

@article{liu2023symmetry,
    title={Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials},
    author={Liu, Shengchao and Du, Weitao and Li, Yanjing and Li, Zhuoxinran and Zheng, Zhiling and Duan, Chenru and Ma, Zhiming and Yaghi, Omar and Anandkumar, Anima and Borgs, Christian and others},
    journal={arXiv preprint arXiv:2306.09375},
    year={2023}
}