https://github.com/DS4SD/PatCID
[Nat. Commun.] PatCID: an open-access dataset of chemical structures in patent documents
https://github.com/DS4SD/PatCID
Last synced: 5 months ago
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[Nat. Commun.] PatCID: an open-access dataset of chemical structures in patent documents
- Host: GitHub
- URL: https://github.com/DS4SD/PatCID
- Owner: DS4SD
- License: mit
- Created: 2024-05-17T10:05:53.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2025-08-19T10:49:02.000Z (11 months ago)
- Last Synced: 2025-08-19T12:39:37.731Z (11 months ago)
- Language: Python
- Homepage:
- Size: 73.3 MB
- Stars: 57
- Watchers: 4
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# PatCID
This is the repository for [PatCID: an open-access dataset of chemical structures in patent documents](https://www.nature.com/articles/s41467-024-50779-y). PatCID is a dataset of molecules linked to the patent document displaying them.
### Citation
If you find this repository useful, please consider citing:
```
@article{Morin2024,
title = {{PatCID: an open-access dataset of chemical structures in patent documents}},
author = {Morin, Lucas and Weber, Val{\'e}ry and Meijer, Gerhard Ingmar and Yu, Fisher and Staar, Peter W. J.},
year = 2024,
month = {Aug},
day = {02},
journal = {Nature Communications},
volume = 15,
number = 1,
pages = 6532,
doi = {10.1038/s41467-024-50779-y},
issn = {2041-1723},
url = {https://doi.org/10.1038/s41467-024-50779-y}
}
```
### Installation
Create a virtual environment.
```
conda create -n patcid python=3.11
conda activate patcid
```
Install poppler.
```
Linux: apt-get install poppler-utils
Mac: brew install poppler
```
Install python dependencies.
```
pip install -e .
```
### Download PatCID Dataset
The [PatCID dataset is available on Zenodo](https://doi.org/10.5281/zenodo.10572870).
```
wget https://zenodo.org/records/10572870/files/patcid.zip?download=1 -O patcid.zip
unzip patcid.zip -d ./data/patcid/
```
(Download size: `5.7 GB`, files format: `.jsonl`)
### Document Retrieval
Run the notebook `./examples/molecule_query.ipynb` to use PatCID to retrieve documents referencing a molecule of interest.
### Molecule Retrieval
Run the notebook `./examples/patent_query.ipynb` to use PatCID to retrieve molecules displayed in a given patent document.
### User Interface
https://github.com/DS4SD/PatCID/assets/73230090/0fc7c2bd-ce1b-4ded-a278-7d9c4c277244
To request access to the above user interface, please contact the IBM's [Deep Search](https://ds4sd.github.io/) team at deepsearch-core@zurich.ibm.com.
### Benchmark Datasets
The benchmarks datasets [D2C-UNI and D2C-RND are available on Zenodo](https://doi.org/10.5281/zenodo.10978812).
### Code
The code repositories used to build and evaluate PatCID are available:
- The [classification model](https://github.com/DS4SD/MolClassifier)
- The [recognition model](https://github.com/DS4SD/MolGrapher)
- The [chemical-structure annotation tool](https://github.com/DS4SD/MolAnnotator).
For segmenting chemical-structure images from documents, we use [DECIMER Segmentation](https://github.com/Kohulan/DECIMER-Image-Segmentation) from K. Rajan, H. O. Brinkhaus, M. Sorokina, A. Zielesny and C. Steinbeck.
### Models
The model weights are available on Hugging Face:
- The [classification model](https://huggingface.co/ds4sd/MolClassifier)
- The [recognition model](https://huggingface.co/ds4sd/MolGrapher).
### Training Datasets
The training datasets are available on Zenodo and Hugging Face:
- The [image classification model dataset](https://doi.org/10.5281/zenodo.10978564)
- The [molecule recognition model dataset](https://huggingface.co/datasets/ds4sd/MolGrapher-Synthetic-300K).
### Additional Visualization
To test our processing pipeline outside its main application domain, we process a [scientific publication](https://chemrxiv.org/engage/chemrxiv/article-details/662d287a91aefa6ce198f9b8) published on ChemRxiv. `./data/extra/scientific_paper_example/` contains the pages of the document (`page_*.png`) annotated with the segmentation and classification predictions. For pages containing molecules, the predicted molecules are provided in `page_*_molecules.txt`.