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https://github.com/thomas0809/molscribe
Robust Molecular Structure Recognition with Image-to-Graph Generation
https://github.com/thomas0809/molscribe
chemistry deep-learning molecule
Last synced: 7 days ago
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Robust Molecular Structure Recognition with Image-to-Graph Generation
- Host: GitHub
- URL: https://github.com/thomas0809/molscribe
- Owner: thomas0809
- License: mit
- Created: 2022-11-10T05:07:55.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-09-22T18:25:33.000Z (3 months ago)
- Last Synced: 2024-12-08T18:10:03.439Z (14 days ago)
- Topics: chemistry, deep-learning, molecule
- Language: Python
- Homepage:
- Size: 69.9 MB
- Stars: 158
- Watchers: 8
- Forks: 33
- Open Issues: 16
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# MolScribe
This is the repository for MolScribe, an image-to-graph model that translates a molecular image to its chemical
structure. Try our [demo](https://huggingface.co/spaces/yujieq/MolScribe) on HuggingFace!
If you use MolScribe in your research, please cite our [paper](https://pubs.acs.org/doi/10.1021/acs.jcim.2c01480).
```
@article{
MolScribe,
title = {{MolScribe}: Robust Molecular Structure Recognition with Image-to-Graph Generation},
author = {Yujie Qian and Jiang Guo and Zhengkai Tu and Zhening Li and Connor W. Coley and Regina Barzilay},
journal = {Journal of Chemical Information and Modeling},
publisher = {American Chemical Society ({ACS})},
doi = {10.1021/acs.jcim.2c01480},
year = 2023,
}
```
Please check out our subsequent works on parsing chemical diagrams:
- [RxnScribe](https://github.com/thomas0809/RxnScribe) (reaction diagram parsing):
[paper](https://pubs.acs.org/doi/10.1021/acs.jcim.3c00439),
[code](https://github.com/thomas0809/RxnScribe), [demo](https://huggingface.co/spaces/yujieq/RxnScribe)
- [OpenChemIE](https://github.com/CrystalEye42/OpenChemIE) (information extraction toolkit for chemistry literature): [paper](https://pubs.acs.org/doi/10.1021/acs.jcim.4c00572), [code](https://github.com/CrystalEye42/OpenChemIE), [demo](https://mit.openchemie.info)
## Quick Start
### Installation
Option 1: Install MolScribe with pip
```
pip install MolScribe
```
Option 2: Run the following command to install the package and its dependencies
```
git clone [email protected]:thomas0809/MolScribe.git
cd MolScribe
python setup.py install
```
### Example
Download the MolScribe checkpoint from [HuggingFace Hub](https://huggingface.co/yujieq/MolScribe/tree/main)
and predict molecular structures:
```python
import torch
from molscribe import MolScribe
from huggingface_hub import hf_hub_download
ckpt_path = hf_hub_download('yujieq/MolScribe', 'swin_base_char_aux_1m.pth')
model = MolScribe(ckpt_path, device=torch.device('cpu'))
output = model.predict_image_file('assets/example.png', return_atoms_bonds=True, return_confidence=True)
```
The output is a dictionary, with the following format
```
{
'smiles': 'Fc1ccc(-c2cc(-c3ccccc3)n(-c3ccccc3)c2)cc1',
'molfile': '***',
'confidence': 0.9175,
'atoms': [{'atom_symbol': '[Ph]', 'x': 0.5714, 'y': 0.9523, 'confidence': 0.9127}, ... ],
'bonds': [{'bond_type': 'single', 'endpoint_atoms': [0, 1], 'confidence': 0.9999}, ... ]
}
```
Please refer to [`molscribe/interface.py`](molscribe/interface.py) and [`notebook/predict.ipynb`](notebook/predict.ipynb)
for details and other available APIs.
For development or reproducing the experiments, please follow the instructions below.
## Experiments
### Requirements
Install the required packages
```
pip install -r requirements.txt
```
### Data
For training or evaluation, please download the corresponding datasets to `data/`.
Training data:
| Datasets | Description |
|-------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------|
| USPTO
[Download](https://huggingface.co/yujieq/MolScribe/blob/main/uspto_mol.zip) | Downloaded from [USPTO, Grant Red Book](https://bulkdata.uspto.gov/). |
| PubChem
[Download](https://huggingface.co/yujieq/MolScribe/blob/main/pubchem.zip) | Molecules are downloaded from [PubChem](https://ftp.ncbi.nlm.nih.gov/pubchem/Compound/), and images are dynamically rendered during training. |
Benchmarks:
| Category | Datasets | Description |
|--------------------------------------------------------------------------------------------|-----------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Synthetic
[Download](https://huggingface.co/yujieq/MolScribe/blob/main/synthetic.zip) | Indigo
ChemDraw | Images are rendered by Indigo and ChemDraw. |
| Realistic
[Download](https://huggingface.co/yujieq/MolScribe/blob/main/real.zip) | CLEF
UOB
USPTO
Staker
ACS | CLEF, UOB, and USPTO are downloaded from https://github.com/Kohulan/OCSR_Review.
Staker is downloaded from https://drive.google.com/drive/folders/16OjPwQ7bQ486VhdX4DWpfYzRsTGgJkSu.
ACS is a new dataset collected by ourself. |
| Perturbed
[Download](https://huggingface.co/yujieq/MolScribe/blob/main/perturb.zip) | CLEF
UOB
USPTO
Staker | Downloaded from https://github.com/bayer-science-for-a-better-life/Img2Mol/ |
### Model
Our model checkpoints can be downloaded from [Dropbox](https://www.dropbox.com/sh/91u508kf48cotv4/AACQden2waMXIqLwYSi8zO37a?dl=0)
or [HuggingFace Hub](https://huggingface.co/yujieq/MolScribe/tree/main).
Model architecture:
- Encoder: [Swin Transformer](https://github.com/microsoft/Swin-Transformer), Swin-B.
- Decoder: Transformer, 6 layers, hidden_size=256, attn_heads=8.
- Input size: 384x384
Download the model checkpoint to reproduce our experiments:
```
mkdir -p ckpts
wget -P ckpts https://huggingface.co/yujieq/MolScribe/resolve/main/swin_base_char_aux_1m680k.pth
```
### Prediction
```
python predict.py --model_path ckpts/swin_base_char_aux_1m680k.pth --image_path assets/example.png
```
MolScribe prediction interface is in [`molscribe/interface.py`](molscribe/interface.py).
See python script [`predict.py`](predict.py) or jupyter notebook [`notebook/predict.ipynb`](notebook/predict.ipynb)
for example usage.
### Evaluate MolScribe
```
bash scripts/eval_uspto_joint_chartok_1m680k.sh
```
The script uses one GPU and batch size of 64 by default. If more GPUs are available, update `NUM_GPUS_PER_NODE` and
`BATCH_SIZE` for faster evaluation.
### Train MolScribe
```
bash scripts/train_uspto_joint_chartok_1m680k.sh
```
The script uses four GPUs and batch size of 256 by default. It takes about one day to train the model with four A100 GPUs.
During training, we use a modified code of [Indigo](https://github.com/epam/Indigo) (included in `molscribe/indigo/`).
### Evaluation Script
We implement a standalone evaluation script [`evaluate.py`](evaluate.py). Example usage:
```
python evaluate.py \
--gold_file data/real/acs.csv \
--pred_file output/uspto/swin_base_char_aux_1m680k/prediction_acs.csv \
--pred_field post_SMILES
```
The prediction should be saved in a csv file, with columns `image_id` for the index (must match the gold file),
and `SMILES` for predicted SMILES. If prediction has a different column name, specify it with `--pred_field`.
The result contains three scores:
- canon_smiles: our main metric, exact matching accuracy.
- graph: graph exact matching accuracy, ignoring tetrahedral chirality.
- chiral: exact matching accuracy on chiral molecules.