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CryoZeta
KiharaLab/CryoZeta
CryoZeta is a machine learning model from KiharaLab. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as other.
CryoZeta is a de novo macromolecular structure modeling tool that integrates cryo-EM density information with a diffusion-model-based structure prediction pipeline.
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Public
Updated Apr 22, 2026
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34.9 GB
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At a glance
- License
- other
- Access
- Public
- Created
- Feb 8, 2026
- Updated
- Apr 22, 2026
- SHA
- 6fdba93a
Model card
From the Hugging Face model README
CryoZeta
CryoZeta is a de novo macromolecular structure modeling tool that integrates cryo-EM density information with a diffusion-model-based structure prediction pipeline.
Kihara Lab website: https://kiharalab.org/
Kihara Lab EM server: https://em.kiharalab.org/algorithm/CryoZeta
Github: https://github.com/kiharalab/CryoZeta
Lincense
This repository uses a custom license. See the LICENSE file for full terms.
Citations
Please cite our paper:
@article{zhang2026accurate,
title = {Accurate macromolecular complex modeling for cryo-EM},
author = {Zhang, Zicong and Li, Shu and Farheen, Farhanaz and Kagaya, Yuki and Liu, Boyuan and Ibtehaz, Nabil and Terashi, Genki and Nakamura, Tsukasa and Zhu, Han and Khan, Kafi and Zhang, Yuanyuan and Kihara, Daisuke},
journal = {bioRxiv},
year = {2026},
doi = {10.64898/2026.02.13.705846},
url = {https://www.biorxiv.org/content/10.64898/2026.02.13.705846v1},
note = {Preprint}
}
- License
- other
- Created
- Feb 8, 2026
- Updated
- Apr 22, 2026