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The official implementation of Learning Gradient Fields for Molecular Conformation Generation (ICML 2021 Long talk)

Installation

Install via Conda (Recommended)

# Clone the environment
conda env create -f env.yml

# Activate the environment
conda activate confgf

# Install Library
git clone https://github.com/DeepGraphLearning/ConfGF.git
cd ConfGF
python setup.py install

Install Manually

# Create conda environment
conda create -n confgf python=3.7

# Activate the environment
conda activate confgf

# Install packages
conda install -y -c pytorch pytorch=1.7.0 torchvision torchaudio cudatoolkit=10.2
conda install -y -c rdkit rdkit==2020.03.2.0
conda install -y scikit-learn pandas decorator ipython networkx tqdm matplotlib
conda install -y -c conda-forge easydict
pip install pyyaml

# Install PyTorch Geometric
pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cu102.html
pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.7.0+cu102.html
pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-1.7.0+cu102.html
pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.7.0+cu102.html
pip install torch-geometric==1.6.3

# Install Library
git clone https://github.com/DeepGraphLearning/ConfGF.git
cd ConfGF
python setup.py install

Dataset

Offical Dataset

The offical raw GEOM dataset is avaiable [here].

Preprocessed dataset

We provide the preprocessed datasets (GEOM, ISO17) in a [google drive folder]. For ISO17 dataset, we use the default split of [GraphDG].

Prepare your own GEOM dataset from scratch (optional)

Download the raw GEOM dataset and unpack it.

tar xvf ~/rdkit_folder.tar.gz -C ~/GEOM

Preprocess the raw GEOM dataset.

python script/process_GEOM_dataset.py --base_path GEOM --dataset_name qm9 --confmin 50 --confmax 500
python script/process_GEOM_dataset.py --base_path GEOM --dataset_name drugs --confmin 50 --confmax 100

The final folder structure will look like this:

GEOM
|___rdkit_folder  # raw dataset
|   |___qm9 # raw qm9 dataset
|   |___drugs # raw drugs dataset
|   |___summary_drugs.json
|   |___summary_qm9.json
|   
|___qm9_processed
|   |___train_data_40k.pkl
|   |___val_data_5k.pkl
|   |___test_data_200.pkl
|   
|___drugs_processed
|   |___train_data_39k.pkl
|   |___val_data_5k.pkl
|   |___test_data_200.pkl
|
iso17_processed
|___iso17_split-0_train_processed.pkl
|___iso17_split-0_test_processed.pkl
|
...

Training

All hyper-parameters and training details are provided in config files (./config/*.yml), and free feel to tune these parameters.

You can train the model with the following commands:

python -u script/train.py --config_path ./config/qm9_default.yml
python -u script/train.py --config_path ./config/drugs_default.yml
python -u script/train.py --config_path ./config/iso17_default.yml

The checkpoint of the models will be saved into a directory specified in config files.

Generation

We provide the checkpoints of three trained models, i.e., qm9_default, drugs_default and iso17_default in a [google drive folder].

You can generate conformations of a molecule by feeding its SMILES into the model:

python -u script/gen.py --config_path ./config/qm9_default.yml --generator ConfGF --smiles c1ccccc1
python -u script/gen.py --config_path ./config/qm9_default.yml --generator ConfGFDist --smiles c1ccccc1

Here we use the models trained on GEOM-QM9 to generate conformations for the benzene. The argument --generator indicates the type of the generator, i.e., ConfGF vs. ConfGFDist. See the ablation study (Table 5) in the original paper for more details.

You can also generate conformations for an entire test set.

python -u script/gen.py --config_path ./config/qm9_default.yml --generator ConfGF \
                        --start 0 --end 200 \

python -u script/gen.py --config_path ./config/qm9_default.yml --generator ConfGFDist \
                        --start 0 --end 200 \

python -u script/gen.py --config_path ./config/drugs_default.yml --generator ConfGF \
                        --start 0 --end 200 \

python -u script/gen.py --config_path ./config/drugs_default.yml --generator ConfGFDist \
                        --start 0 --end 200 \

Here start and end indicate the range of the test set that we want to use. All hyper-parameters related to generation can be set in config files.

Conformations of some drug-like molecules generated by ConfGF are provided below.

Get Results

The results of all benchmark tasks can be calculated based on generated conformations.

We report the results of each task in the following tables. Results of ConfGF and ConfGFDist are re-evaluated based on the current code base, which successfully reproduce the results reported in the original paper. Results of other models are taken directly from the original paper.

Task 1. Conformation Generation

The COV and MAT scores on the GEOM datasets can be calculated using the following commands:

python -u script/get_task1_results.py --input dir_of_QM9_samples --core 10 --threshold 0.5  

python -u script/get_task1_results.py --input dir_of_Drugs_samples --core 10 --threshold 1.25  

Table: COV and MAT scores on GEOM-QM9

QM9	COV-Mean (%)	COV-Median (%)	MAT-Mean (\AA)	MAT-Median (\AA)
ConfGF	91.06	95.76	0.2649	0.2668
ConfGFDist	85.37	88.59	0.3435	0.3548
CGCF	78.05	82.48	0.4219	0.3900
GraphDG	73.33	84.21	0.4245	0.3973
CVGAE	0.09	0.00	1.6713	1.6088
RDKit	83.26	90.78	0.3447	0.2935

Table: COV and MAT scores on GEOM-Drugs

Drugs	COV-Mean (%)	COV-Median (%)	MAT-Mean (\AA)	MAT-Median (\AA)
ConfGF	62.54	71.32	1.1637	1.1617
ConfGFDist	49.96	48.12	1.2845	1.2827
CGCF	53.96	57.06	1.2487	1.2247
GraphDG	8.27	0.00	1.9722	1.9845
CVGAE	0.00	0.00	3.0702	2.9937
RDKit	60.91	65.70	1.2026	1.1252

Task 2. Distributions Over Distances

The MMD metrics on the ISO17 dataset can be calculated using the following commands:

python -u script/get_task2_results.py --input dir_of_ISO17_samples

Table: Distributions over distances

Method	Single-Mean	Single-Median	Pair-Mean	Pair-Median	All-Mean	All-Median
ConfGF	0.3430	0.2473	0.4195	0.3081	0.5432	0.3868
ConfGFDist	0.3348	0.2011	0.4080	0.2658	0.5821	0.3974
CGCF	0.4490	0.1786	0.5509	0.2734	0.8703	0.4447
GraphDG	0.7645	0.2346	0.8920	0.3287	1.1949	0.5485
CVGAE	4.1789	4.1762	4.9184	5.1856	5.9747	5.9928
RDKit	3.4513	3.1602	3.8452	3.6287	4.0866	3.7519

Visualizing molecules with PyMol

Start Setup

1. pymol -R
2. Display - Background - White
3. Display - Color Space - CMYK
4. Display - Quality - Maximal Quality
5. Display Grid
   1. by object: use set grid_slot, int, mol_name to put the molecule into the corresponding slot
   2. by state: align all conformations in a single slot
   3. by object-state: align all conformations and put them in separate slots. (grid_slot dont work!)
6. Setting - Line and Sticks - Ball and Stick on - Ball and Stick ratio: 1.5
7. Setting - Line and Sticks - Stick radius: 0.2 - Stick Hydrogen Scale: 1.0

Show Molecule

1. To show molecules

   1. hide everything
   2. show sticks

2. To align molecules: align name1, name2

3. Convert RDKit mol to Pymol

   from rdkit.Chem import PyMol
   v= PyMol.MolViewer()
   rdmol = Chem.MolFromSmiles('C')
   v.ShowMol(rdmol, name='mol')
   v.SaveFile('mol.pkl')

Make the trajectory for Langevin dynamics

1. load a sequence of pymol objects named traj*.pkl into the PyMol, where traji.pkl is the i-th conformation in the trajectory.
2. Join states: join_states mol, traj*, 0
3. Delete useless object: delete traj*
4. Movie - Program - State Loop - Full Speed
5. Export the movie to a sequence of PNG files: File - Export Movie As - PNG Images
6. Use photoshop to convert the PNG sequence to a GIF with the transparent background.

Citation

Please consider citing the following paper if you find our codes helpful. Thank you!

@inproceedings{shi*2021confgf,
title={Learning Gradient Fields for Molecular Conformation Generation},
author={Shi, Chence and Luo, Shitong and Xu, Minkai and Tang, Jian},
booktitle={International Conference on Machine Learning},
year={2021}
}

Contact

Chence Shi (chence.shi@umontreal.ca)

Shitong Luo (luost26@gmail.com)