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https://github.com/yuyangw/imolclr
Implementation of iMolCLR: "Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast" in PyG.
https://github.com/yuyangw/imolclr
deep-learning graph-neural-networks molecule pytorch pytorch-geometric self-supervised-learning
Last synced: 29 days ago
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Implementation of iMolCLR: "Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast" in PyG.
- Host: GitHub
- URL: https://github.com/yuyangw/imolclr
- Owner: yuyangw
- License: mit
- Created: 2022-04-17T20:39:53.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2022-08-30T19:00:52.000Z (over 2 years ago)
- Last Synced: 2023-04-03T15:24:50.820Z (over 1 year ago)
- Topics: deep-learning, graph-neural-networks, molecule, pytorch, pytorch-geometric, self-supervised-learning
- Language: Python
- Homepage:
- Size: 8.59 MB
- Stars: 11
- Watchers: 1
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast ##
#### Journal of Chemical Information and Modeling [[Paper]](https://pubs.acs.org/doi/full/10.1021/acs.jcim.2c00495) [[arXiv]](https://arxiv.org/abs/2202.09346) [[PDF]](https://arxiv.org/pdf/2202.09346.pdf)
[Yuyang Wang](https://yuyangw.github.io/), [Rishikesh Magar](https://www.linkedin.com/in/rishikesh-magar), Chen Liang, [Amir Barati Farimani](https://www.meche.engineering.cmu.edu/directory/bios/barati-farimani-amir.html) Carnegie Mellon University
This is the offical implementation of iMolCLR: ["Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast"](https://pubs.acs.org/doi/full/10.1021/acs.jcim.2c00495).
If you find our work useful in your research, please cite:
```
@article{wang2022improving,
title={Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast},
author={Wang, Yuyang and Magar, Rishikesh and Liang, Chen and Farimani, Amir Barati},
journal={Journal of Chemical Information and Modeling},
volume={59},
number={8},
pages={3370--3388},
year={2022},
publisher={ACS Publications},
doi={10.1021/acs.jcim.2c00495}
}
@article{wang2022molclr,
title={Molecular contrastive learning of representations via graph neural networks},
author={Wang, Yuyang and Wang, Jianren and Cao, Zhonglin and Barati Farimani, Amir},
journal={Nature Machine Intelligence},
pages={1--9},
year={2022},
publisher={Nature Publishing Group},
doi={10.1038/s42256-022-00447-x}
}
```
## Getting Started
### Installation
Set up conda environment and clone the github repo
```
# create a new environment
$ conda create --name imolclr python=3.7
$ conda activate imolclr
# install requirements
$ pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 -f https://download.pytorch.org/whl/torch_stable.html
$ pip install torch-geometric==1.6.3 torch-sparse==0.6.9 torch-scatter==2.0.6 -f https://pytorch-geometric.com/whl/torch-1.7.0+cu110.html
$ pip install PyYAML
$ conda install -c conda-forge rdkit=2021.09.1
$ conda install -c conda-forge tensorboard
# clone the source code of iMolCLR
$ git clone https://github.com/yuyangw/iMolCLR.git
$ cd iMolCLR
```
### Dataset
You can download the pre-training data and benchmarks used in the paper [here](https://drive.google.com/file/d/1aDtN6Qqddwwn2x612kWz9g0xQcuAtzDE/view?usp=sharing) and extract the zip file under `./data` folder. The data for pre-training can be found in `pubchem-10m-clean.txt`. All the databases for fine-tuning are saved in the folder under the benchmark name. You can also find the benchmarks from [MoleculeNet](https://moleculenet.org/).
### Pre-training
To train the iMolCLR, where the configurations are defined in `config.yaml`
```
$ python imolclr.py
```
To monitor the training via tensorboard, run `tensorboard --logdir ckpt/{PATH}` and click the URL http://127.0.0.1:6006/.
### Fine-tuning
To fine-tune the iMolCLR pre-trained model on downstream molecular benchmarks, where the configurations are defined in `config_finetune.yaml`
```
$ python finetune.py
```
### Pre-trained model
We also provide a pre-trained model, which can be found in `ckpt/pretrained`. You can load the model by change the `fine_tune_from` variable in `config_finetune.yaml` to `pretrained`.