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https://github.com/DeepGraphLearning/ConfGF
Implementation of Learning Gradient Fields for Molecular Conformation Generation (ICML 2021).
https://github.com/DeepGraphLearning/ConfGF
Last synced: about 2 months ago
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Implementation of Learning Gradient Fields for Molecular Conformation Generation (ICML 2021).
- Host: GitHub
- URL: https://github.com/DeepGraphLearning/ConfGF
- Owner: DeepGraphLearning
- License: mit
- Created: 2021-06-07T06:33:00.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2021-09-29T02:25:38.000Z (almost 3 years ago)
- Last Synced: 2024-06-14T01:31:17.788Z (3 months ago)
- Language: Python
- Homepage:
- Size: 3.3 MB
- Stars: 154
- Watchers: 10
- Forks: 34
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- top-life-sciences - **DeepGraphLearning/ConfGF**
README
----------------------------
[](https://github.com/DeepGraphLearning/ConfGF/blob/main/LICENSE)
[[PDF]](https://arxiv.org/abs/2105.03902) | [[Slides]](https://drive.google.com/file/d/1wA5Qu98dYPmEdoGt1QQcYfoUJG3Ndnec/view?usp=sharing)
The official implementation of Learning Gradient Fields for Molecular Conformation Generation (ICML 2021 **Long talk**)
## Installation
### Install via Conda (Recommended)
```bash
# 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
```bash
# 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]](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JNGTDF).
### Preprocessed dataset
We provide the preprocessed datasets (GEOM, ISO17) in a [[google drive folder]](https://drive.google.com/drive/folders/10dWaj5lyMY0VY4Zl0zDPCa69cuQUGb-6?usp=sharing).
For ISO17 dataset, we use the default split of [[GraphDG]](https://github.com/gncs/graphdg).
### Prepare your own GEOM dataset from scratch (optional)
Download the raw GEOM dataset and unpack it.
```bash
tar xvf ~/rdkit_folder.tar.gz -C ~/GEOM
```
Preprocess the raw GEOM dataset.
```bash
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:
```bash
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]](https://drive.google.com/drive/folders/10dWaj5lyMY0VY4Zl0zDPCa69cuQUGb-6?usp=sharing).
You can generate conformations of a molecule by feeding its SMILES into the model:
```bash
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.
```bash
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:
```bash
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:
```bash
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
```python
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 ([email protected])
Shitong Luo ([email protected])