Metadata-Version: 2.1
Name: nucleofind
Version: 1.2.0
Summary: NucleoFind: A Deep-Learning Network for Interpreting Nucleic Acid Electron Density
Author-Email: Jordan Dialpuri <jordan.dialpuri@york.ac.uk>
Classifier: License :: OSI Approved :: BSD License
Project-URL: Homepage, https://github.com/Dialpuri/NucleoFind
Requires-Python: >=3.9
Requires-Dist: onnxruntime-gpu; platform_system != "Darwin"
Requires-Dist: onnxruntime; platform_system == "Darwin"
Requires-Dist: tqdm
Requires-Dist: gemmi
Requires-Dist: numpy
Requires-Dist: requests
Description-Content-Type: text/markdown

# NucleoFind <img src="https://github.com/Dialpuri/NucleoFind/assets/44945647/a7c6c30c-a9fb-4f30-bdc0-3705ae9df36f" alt="logo" width="50"/> 

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Nucleic acid electron density interpretation remains a difficult problem for computer programs to deal with. Programs tend to rely on exhaustive searches to recognise characteristic features. NucleoFind is a deep-learning-based approach to interpreting and segmenting electron density. Using a crystallographic map, the positions of the phosphate group, sugar ring and nitrogenous base group are able to be predicted with high accuracy. 

## Documentation
[Link to Documentation](https://dialpuri.github.io/NucleoFind/about-nucleofind.html)

## Publications

If you find _NucleoFind_ useful, please cite: 

- Jordan S Dialpuri, Jon Agirre, Kathryn D Cowtan, Paul S Bond, NucleoFind: A Deep-Learning Network for Interpreting Nucleic Acid Electron Density, Nucleic Acid Research, 2024 [https://doi.org/10.1093/nar/gkae715](https://doi.org/10.1093/nar/gkae715)
