3D-Transformer: Molecular Representation with Transformer in 3D Space

Introduction

This is the repository for 3D-Transformer.

Dataset

Quantum Chemistry

QM7 Dataset
Download (Official Website): http://quantum-machine.org/datasets/
Discription (DeepChem): https://deepchem.readthedocs.io/en/latest/api_reference/moleculenet.html#qm7-datasets

QM8 Dataset
Download (DeepChem): https://github.com/deepchem/deepchem/blob/master/deepchem/molnet/load_function/qm8_datasets.py
Discription (DeepChem): https://deepchem.readthedocs.io/en/latest/api_reference/moleculenet.html?highlight=qm7#qm8-datasets

QM9 Dataset Download (Atom3D): https://www.atom3d.ai/smp.html
Download (Official Website): https://ndownloader.figshare.com/files/3195389
Download (MPNN Supplement): https://drive.google.com/file/d/0Bzn36Iqm8hZscHFJcVh5aC1mZFU/view?resourcekey=0-86oyPL3e3l2ZTiRpwtPDBg
Download (Schnet): https://schnetpack.readthedocs.io/en/stable/tutorials/tutorial_02_qm9.html#Loading-the-data

GEOM-QM9 Dataset Download (Official Website): https://doi.org/10.7910/DVN/JNGTDF Tutorial of usage: https://github.com/learningmatter-mit/geom/blob/master/tutorials/01_loading_data.ipynb

Material Science

COREMOF
Download (Google Drive): https://drive.google.com/drive/folders/1DMmjL-JNgUWQDU-52_DT_cX-XWNEEi-W?usp=sharing
Reproduction of PointNet++: python coremof/reproduce/main_pn_coremof.py
Reproduction of MPNN: python coremof/reproduce/main_mpnn_coremof.py
Repredoction of SchNet: (1) load COREMOF python coremof/reproduce/main_sch_coremof.py
(2) run SchNet spk_run.py train schnet custom ../../coremof.db ./coremof --split 900 100 --property LCD --features 16 --batch_size 20 --cuda
(Note: official script of Schnet cannot be reproduced successfully due to the memory limitation.)

Protein

PDBbind
Atom3d: https://github.com/drorlab/atom3d
(1) download 'split-by-sequence-identity-30' dataset from https://www.atom3d.ai/
(2) install atom3D pip install atom3d
(3) preprocess the data by running python pdbbind/dataloader_pdb.py

Models

models/tr_spe: 3D-Transformer with Sinusoidal Position Encoding (SPE)
models/tr_cpe: 3D-Transformer with Convolutional Position Encoding (CPE)
models/tr_msa: 3D-Transformer with Multi-scale Self-attention (MSA)
models/tr_afps: 3D-Transformer with Attentive Farthest Point Sampling (AFPS)
models/tr_full: 3D-Transformer with CPE + MAS + AFPS