Protenix: Protein + X

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.

🌟 Related Projects

• 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.

• 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.

• 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.

🎉 Latest Updates

• 2026-04-08: Protenix-v2 Released 💪💪 [Protenix-v2 Technical Report]
  • Protenix-v2 shows clear gains on antibody-antigen structure prediction, together with an additional update in ligand-related plausibility.
• 2026-02-05: Protenix-v1 Released 💪 [Protenix-v1 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.

🚀 Getting Started

🛠 Quick Installation

pip install protenix

🧬 Quick Prediction

# Predict structure using a JSON input
protenix pred -i examples/input.json -o ./output -n protenix_base_default_v1.0.0

Key Model Descriptions

• protenix-v2: An enhanced-capacity version of the base model, featuring increased representation dimensionality and expanded parameter space (~464M), along with substantial training and optimization improvements.
• protenix_base_default_v1.0.0: Base model, trained with a data cutoff aligned with AlphaFold3 (2021-09-30). The total parameter count of protenix_base_default_v1.0.0 is close to that of AlphaFold3.
• 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.

📊 Benchmark

Protenix-v2

Protenix-v2 (refers to the protenix-v2 model) shows clear gains on antibody-antigen structure prediction, together with an additional update in ligand-related plausibility. Compared to baselines and the earlier Protenix-v1, Protenix-v2 demonstrates a substantial improvement trend. At the DockQ > 0.23 threshold, Protenix-v2 achieves absolute success rate gains of 9 to 13 percentage points over Protenix-v1 across three collections. Remarkably, Protenix-v2 at only 5 seeds already exceeds the performance of Protenix-v1 at 1000 seeds, indicating a clear gain in efficiency.

Protenix-v1

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.

Citing Protenix

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}
}

📚 Citing Related Work

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}
}

Contributing to Protenix

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.

Acknowledgements

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.

Code of Conduct

We are committed to fostering a welcoming and inclusive environment. Please review our Code of Conduct for guidelines on how to participate respectfully.

Security

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.

License

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.

Contact Us

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.