reaction_yield_pretrained_gnn

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reaction_yield_pretrained_gnn

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Source code for "Improving Chemical Reaction Yield Prediction Using Pre-Trained Graph Neural Networks"

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# reaction_yield_pretrained_gnn
Source code for the paper: Improving Chemical Reaction Yield Prediction Using Pre-Trained Graph Neural Networks

## Data
- The datasets used in the paper
  - Pre-Training Dataset (10M mols collected from Pubchem) - https://arxiv.org/pdf/2010.09885.pdf
  - Chemical Reaction Yield Benchmark Datasets - https://github.com/rxn4chemistry/rxn_yields/

## Components
- **data/get_pretraining_data.py** - pre-training dataset preprocessing functions
- **data/get_reaction_yield_data.py** - chemical reaction yield benchmark dataset preprocessing functions
- **src/dataset.py** - data structure & functions
- **src/model.py** - model architecture & training / inference functions
- **src/pretrain.py** - pre-training functions
- **src/finetune.py** - fine-tuning functions
- **src/preprocess_util.py** - util functions for data preprocessing
- **src/util.py** - util functions for model training / inference
- **main_pretrain.py** - script for pre-training
- **main_finetune.py** - script for fine-tuning
- **run.sh** - run code example

## Dependencies
- **Python**
- **Pytorch**
- **DGL**
- **RDKit**
- **Mordred**

## Citation
```
@Article{reaction_yield_pretrained_gnn,
  title={Improving chemical reaction yield prediction using pre-trained graph neural networks},
  author={Han, Jongmin and Kwon, Youngchun and Choi, Youn-Suk and Kang, Seokho},
  journal={Journal of Cheminformatics},
  volume={16},
  number={25}
  year={2024},
  doi={10.1186/s13321-024-00818-z}
}
```

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