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

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

Setup the anaconda

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

Start with some basic packages.

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

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, please refer to the data folder.

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.

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