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We’re excited to introduce Protenix — Toward High-Accuracy Open-Source Biomolecular Structure Prediction.
Protenix is built for high-accuracy structure prediction. It serves as an initial step in our journey toward advancing accessible and extensible research tools for the computational biology community.
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PXDesign is a model suite for de novo protein-binder design built on the Protenix foundation model. PXDesign achieves 20–73% experimental success rates across multiple targets — 2–6× higher than prior SOTA methods such as AlphaProteo and RFdiffusion. The framework is freely accessible via the Protenix Server.
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PXMeter is an open-source toolkit designed for reproducible evaluation of structure prediction models, released with high-quality benchmark dataset that has been manually reviewed to remove experimental artifacts and non-biological interactions. The associated study presents an in-depth comparative analysis of state-of-the-art models, drawing insights from extensive metric data and detailed case studies. The evaluation of Protenix is based on PXMeter.
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Protenix-Dock: Our implementation of a classical protein-ligand docking framework that leverages empirical scoring functions. Without using deep neural networks, Protenix-Dock delivers competitive performance in rigid docking tasks.
- 2026-02-05: Protenix-v1 Released 💪 [Technical Report]
- Supported Template/RNA MSA features and improved training dynamics, along with further Inference-time model performance enhancements.
- 2025-11-05: Protenix-v0.7.0 Released 🚀
- Introduced advanced diffusion inference optimizations: Shared variable caching, efficient kernel fusion, and TF32 acceleration. See our performance analysis.
- 2025-07-17: Protenix-Mini & Constraint Features
- Released lightweight model variants (Protenix-Mini) that drastically reduce inference costs with minimal accuracy loss.
- Added support for atom-level contact and pocket constraints, enhancing prediction accuracy through physical priors.
- 2025-01-16: Pipeline Enhancements
- Open-sourced the full training data pipeline and MSA pipeline.
- Integrated local ColabFold-compatible search for streamlined MSA generation.
pip install protenix# Predict structure using a JSON input
protenix pred -i examples/input.json -o ./output -n protenix_base_default_v1.0.0| Model Name | MSA | RNA MSA | Template | Params | Training Data Cutoff | Model Release Date |
|---|---|---|---|---|---|---|
protenix_base_default_v1.0.0 |
✅ | ✅ | ✅ | 368 M | 2021-09-30 | 2026-02-05 |
protenix_base_20250630_v1.0.0 |
✅ | ✅ | ✅ | 368 M | 2025-06-30 | 2026-02-05 |
protenix_base_default_v0.5.0 |
✅ | ❌ | ❌ | 368 M | 2021-09-30 | 2025-05-30 |
- protenix_base_default_v1.0.0: Default model, trained with a data cutoff aligned with AlphaFold3 (2021-09-30).
💡 This is the highly recommended model for conducting fair, rigorous public benchmarks and comparative studies against other state-of-the-art methods.
- protenix_base_20250630_v1.0.0: Applied model, trained with an updated data cutoff (2025-06-30) for better practical performance. This model can be used for practical application scenarios.
- protenix_base_default_v0.5.0: Previous version of the model, maintained primarily for backward compatibility with users who developed based on v0.5.0.
For a complete list of supported models, please refer to Supported Models.
For detailed instructions on installation, data preprocessing, inference, and training, please refer to the Training and Inference Instructions. We recommend users refer to inference_demo.sh for detailed inference methods and input explanations.
Protenix-v1 (refers to the protenix_base_default_v1.0.0 model), the first fully open-source model that outperforms AlphaFold3 across diverse benchmark sets while adhering to the same training data cutoff, model scale, and inference budget as AlphaFold3. For challenging targets, such as antigen-antibody complexes, the prediction accuracy of Protenix-v1 can be further enhanced through inference-time scaling – increasing the sampling budget from several to hundreds of candidates leads to consistent log-linear gains.
For detailed benchmark metrics on each dataset, please refer to docs/model_1.0.0_benchmark.md.
If you use Protenix in your research, please cite the following:
@article {Zhang2026.02.05.703733,
author = {Zhang, Yuxuan and Gong, Chengyue and Zhang, Hanyu and Ma, Wenzhi and Liu, Zhenyu and Chen, Xinshi and Guan, Jiaqi and Wang, Lan and Yang, Yanping and Xia, Yu and Xiao, Wenzhi},
title = {Protenix-v1: Toward High-Accuracy Open-Source Biomolecular Structure Prediction},
elocation-id = {2026.02.05.703733},
year = {2026},
doi = {10.64898/2026.02.05.703733},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2026/02/22/2026.02.05.703733.1},
eprint = {https://www.biorxiv.org/content/early/2026/02/22/2026.02.05.703733.1.full.pdf},
journal = {bioRxiv}
}
Protenix is built upon and inspired by several influential projects. If you use Protenix in your research, we also encourage citing the following foundational works where appropriate:
@article{abramson2024accurate,
title={Accurate structure prediction of biomolecular interactions with AlphaFold 3},
author={Abramson, Josh and Adler, Jonas and Dunger, Jack and Evans, Richard and Green, Tim and Pritzel, Alexander and Ronneberger, Olaf and Willmore, Lindsay and Ballard, Andrew J and Bambrick, Joshua and others},
journal={Nature},
volume={630},
number={8016},
pages={493--500},
year={2024},
publisher={Nature Publishing Group UK London}
}
@article{ahdritz2024openfold,
title={OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization},
author={Ahdritz, Gustaf and Bouatta, Nazim and Floristean, Christina and Kadyan, Sachin and Xia, Qinghui and Gerecke, William and O’Donnell, Timothy J and Berenberg, Daniel and Fisk, Ian and Zanichelli, Niccol{\`o} and others},
journal={Nature Methods},
volume={21},
number={8},
pages={1514--1524},
year={2024},
publisher={Nature Publishing Group US New York}
}
@article{mirdita2022colabfold,
title={ColabFold: making protein folding accessible to all},
author={Mirdita, Milot and Sch{\"u}tze, Konstantin and Moriwaki, Yoshitaka and Heo, Lim and Ovchinnikov, Sergey and Steinegger, Martin},
journal={Nature methods},
volume={19},
number={6},
pages={679--682},
year={2022},
publisher={Nature Publishing Group US New York}
}
We welcome contributions from the community to help improve Protenix!
📄 Check out the Contributing Guide to get started.
✅ Code Quality:
We use pre-commit hooks to ensure consistency and code quality. Please install them before making commits:
pip install pre-commit
pre-commit install🐞 Found a bug or have a feature request? Open an issue.
The implementation of LayerNorm operators refers to both OneFlow and FastFold.
We also adopted several module implementations from OpenFold, except for LayerNorm, which is implemented independently.
We are committed to fostering a welcoming and inclusive environment. Please review our Code of Conduct for guidelines on how to participate respectfully.
If you discover a potential security issue in this project, or think you may have discovered a security issue, we ask that you notify Bytedance Security via our security center or vulnerability reporting email.
Please do not create a public GitHub issue.
The Protenix project including both code and model parameters is released under the Apache 2.0 License. It is free for both academic research and commercial use.
We welcome inquiries and collaboration opportunities for advanced applications of our model, such as developing new features, fine-tuning for specific use cases, and more. Please feel free to contact us at ai4s-bio@bytedance.com.
We're expanding the Protenix team at ByteDance Seed-AI for Science! We’re looking for talented individuals in machine learning and computational biology/chemistry (“Computational Biology/Chemistry” covers structural biology, computational biology, computational chemistry, drug discovery, and more). Opportunities are available in both Beijing and Seattle, across internships, new grad roles, and experienced full-time positions.
Outstanding applicants will be considered for ByteDance’s Top Seed Talent Program — with enhanced support.
| Type | Expertise | Apply Link |
|---|---|---|
| Full-Time | Protein Design Scientist | Experienced |
| Full-Time | Computational Biology / Chemistry | Experienced, New Grad |
| Full-Time | Machine Learning | Experienced, New Grad |
| Internship | Computational Biology / Chemistry | Internship |
| Internship | Machine Learning | Internship |
| Type | Expertise | Apply Link |
|---|---|---|
| Full-Time | Computational Biology / Chemistry | Experienced, New Grad |
| Full-Time | Machine Learning | Experienced, New Grad |
| Internship | Computational Biology / Chemistry | Internship |
| Internship | Machine Learning | Internship |