https://github.com/scilifelabdatacentre/streamlit-image-to-smiles
Web app that allows users to predict SMILES for chemical structures depicted in images files.
https://github.com/scilifelabdatacentre/streamlit-image-to-smiles
Last synced: about 1 month ago
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Web app that allows users to predict SMILES for chemical structures depicted in images files.
- Host: GitHub
- URL: https://github.com/scilifelabdatacentre/streamlit-image-to-smiles
- Owner: ScilifelabDataCentre
- License: mit
- Created: 2024-11-11T06:52:50.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-01-09T08:28:26.000Z (4 months ago)
- Last Synced: 2025-01-24T19:19:40.798Z (3 months ago)
- Language: Python
- Size: 46.9 KB
- Stars: 0
- Watchers: 3
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Predict SMILES encodings of chemical structure depictions in images
This repository contains code for a web app that allows users to either upload an image file or take a picture using their webcam and get a prediction the chemical structure depicted in the image in SMILES notation.
This application was built using the [Streamlit](https://github.com/streamlit/streamlit) framework (Apache 2.0 license). It is using the [DECIMER Image Transformer](https://github.com/Kohulan/DECIMER-Image_Transformer) (MIT license) model to make predictions (as implemented in the [DECIMER Python package](https://pypi.org/project/decimer/)). In addition, the application allows to edit the predicted SMILES using the web-based
molecule sketcher [Ketcher](https://github.com/epam/ketcher) (Apache 2.0 license).
The live app can be found here: [image-to-smiles.serve.scilifelab.se](https://image-to-smiles.serve.scilifelab.se/).
## Model behind the app
The DECIMER Image Transformer model was developed by the [Cheminformatics and Computational Metabolomics research group](https://cheminf.uni-jena.de/) at Friedrich Schiller University Jena, Germany. You can find out more about the model in these publications:
- Rajan K, et al. "DECIMER.ai - An open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications." *Nat. Commun.* 14, 5045 (2023).
- Rajan, K., et al. "DECIMER 1.0: deep learning for chemical image recognition using transformers." *J Cheminform* 13, 61 (2021).
- Rajan, K., et al. "Advancements in hand-drawn chemical structure recognition through an enhanced DECIMER architecture," *J Cheminform* 16, 78 (2024).
## Contributing
We welcome suggestions and contributions. If you found a mistake or would like to make a suggestion, please create an issue in this repository. Those who wish are also welcome to submit pull requests.
## Contact
This web app was built by [SciLifeLab Data Centre](https://github.com/ScilifelabDataCentre) team members.