https://github.com/hicai-zju/imold
Official implementation for Learning Invariant Molecular Representation in Latent Discrete Space (NeurIPS 2023)
https://github.com/hicai-zju/imold
molecular-representation-learning out-of-distribution
Last synced: 4 months ago
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Official implementation for Learning Invariant Molecular Representation in Latent Discrete Space (NeurIPS 2023)
- Host: GitHub
- URL: https://github.com/hicai-zju/imold
- Owner: HICAI-ZJU
- License: mit
- Created: 2023-10-09T11:24:46.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-10-16T06:23:53.000Z (over 1 year ago)
- Last Synced: 2024-11-14T00:36:28.429Z (6 months ago)
- Topics: molecular-representation-learning, out-of-distribution
- Language: Python
- Homepage:
- Size: 279 KB
- Stars: 19
- Watchers: 1
- Forks: 2
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Learning Invariant Molecular Representation in Latent Discrete Space
This repository is the official implementation of our paper:
**Learning Invariant Molecular Representation in Latent Discrete Space**
_Xiang Zhuang, Qiang Zhang*, Keyan Ding, Yatao Bian, Xiao Wang, Jingsong Lv, Hongyang Chen, Huajun Chen* (* denotes correspondence)_
Advances in Neural Information Processing Systems (NeurIPS) 2023
## Environment
To run the code successfully, the following dependencies need to be installed:
```
Python 3.8
torch 1.10.1
torch_geometric 2.0.4
torch_scatter 2.0.9
torch_cluster 1.6.0
torch_sparse 0.6.13
torch_spline_conv 1.2.1
rdkit_pypi 2022.9.5
vector_quantize_pytorch 1.0.7
ogb 1.3.6
```
This repo is also depended on `GOOD` and `DrugOOD`, please follow the installation methods provided for each package:
- GOOD (Version 1.1.1)
- Repository: https://github.com/divelab/GOOD/
- Installation: Please follow the instructions provided in the repository to install.
- DrugOOD (Version 0.0.1)
- Repository: https://github.com/tencent-ailab/DrugOOD
- Installation: Please follow the instructions provided in the repository to install.
## Data
The data used in the experiments can be downloaded from the following sources:
1. GOOD
- [GOODPCBA](https://drive.google.com/file/d/1WGieOjtgNXtGoO6o1EGhKrZj0zWU7AJl/view?usp=sharing)
- [GOODHIV](https://drive.google.com/file/d/1CoOqYCuLObnG5M0D8a2P2NyL61WjbCzo/view?usp=sharing)
- [GOODZINC](https://drive.google.com/file/d/1CHR0I1JcNoBqrqFicAZVKU3213hbsEPZ/view?usp=sharing)
- Extract the downloaded files and save the contents in the `data` directory.
2. DrugOOD
- download from [link](https://drive.google.com/drive/folders/19EAVkhJg0AgMx7X-bXGOhD4ENLfxJMWC).
- Extract the downloaded file and save the contents in the `drugood-data-chembl30` directory.
An example of the folder hierarchy after adding the data files:
```
├── data
│ ├── GOODHIV
│ ├── GOODPCBA
│ ├── GOODZINC
├── drugood-data-chembl30
│ ├── lbap_core_ec50_assay.json
│ └── ...
├── models
│ ├── model.py
│ └── ...
├── run.py
└── README.md
```
## Running Script
#### Training
```
python run.py --dataset GOODZINC --domain scaffold --shift concept --num_e 4000 --bs 256 --gamma 0.5 --inv_w 0.01 --reg_w 0.5 --gpu 0 --exp_name ZINC --exp_id scaffold-concept
```
Running parameters and descriptions are as follows:
| Parameter | Description | Choices |
| --- | --- | --- |
| dataset | name of dataset | `GOODHIV`, `GOODZINC`, `GOODPCBA`, `ic50_assay`, `ic50_scaffold`, `ic50_size`, `ec50_assay`, `ec50_scaffold`, `ec50_size`.|
| domain | environment-splitting strategy | `scaffold`, `size`. Only need to be specified for datasets in `GOOD`. |
| shift | type of distribution shift | `covariate`, `concept`. Only need to be specified for datasets in `GOOD`. |
| num_e | code book size | - |
| bs | batch size | - |
| gamma | threshold $\gamma$ | - |
| inv_w | $\lambda_1$ | - |
| reg_w | $\lambda_2$ | - |
| gpu | which GPU to use | - |
| exp_name | experiment name | - |
| exp_id | experiment ID | - |
#### Evaluation
We provide the hyperparameters for the training of each dataset in the Appendix, and provide the corresponding checkpoints in the [release page](https://github.com/HICAI-ZJU/iMoLD/releases).
```
python eval.py --dataset GOODZINC --domain scaffold --shift concept --load_path checkpoint/GOODZINC-scaffold-concept.pkl
```
The `load_path` parameter specifies the path to load the checkpoint.
## Citation
If you use or extend our work, please cite the paper as follows:
```bibtex
@InProceedings{zhuang2023learning,
title={Learning Invariant Molecular Representation in Latent Discrete Space},
author={Xiang Zhuang and Qiang Zhang and Keyan Ding and Yatao Bian and Xiao Wang and Jingsong Lv and Hongyang Chen and Huajun Chen},
booktile={Advances in Neural Information Processing Systems},
year={2023}
}
```