# Protenix **Repository Path**: ByteDance/Protenix ## Basic Information - **Project Name**: Protenix - **Description**: No description available - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 3 - **Forks**: 2 - **Created**: 2024-11-09 - **Last Updated**: 2026-02-12 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Protenix: Protein + X > πŸ“£πŸ“£πŸ“£ **We're hiring!** \ > Positions in **_Beijing_** πŸ‡¨πŸ‡³ and **_Seattle_** πŸ‡ΊπŸ‡Έ \ > Interested in machine learning, computational chemistry/biology, structural biology, or drug discovery? Join us to build cutting-edge AI for biology! \ > πŸ‘‰ [**Join us Β»**](#join-us)
⚑ Protenix Web Server • πŸ“„ Technical Report
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We’re excited to introduce **Protenix** β€” 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. Protenix predictions ## 🌟 Related Projects - **[PXDesign](https://protenix.github.io/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 β€” 2–6Γ— higher than prior SOTA methods such as AlphaProteo and RFdiffusion. The framework is freely accessible via the Protenix Server. - **[PXMeter](https://github.com/bytedance/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](https://github.com/bytedance/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. ## πŸŽ‰ Latest Updates - **2026-02-05: Protenix-v1 Released** πŸ’ͺ [[Technical Report](docs/PTX_V1_Technical_Report_202602042356.pdf)] - 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** πŸš€ - Introduced advanced diffusion inference optimizations: Shared variable caching, efficient kernel fusion, and TF32 acceleration. See our [performance analysis](./assets/inference_time_vs_ntoken.png). - **2025-07-17: Protenix-Mini & Constraint Features** - Released lightweight model variants ([Protenix-Mini](https://arxiv.org/abs/2507.11839)) that drastically reduce inference costs with minimal accuracy loss. - Added support for [atom-level contact and pocket constraints](docs/infer_json_format.md#constraint), enhancing prediction accuracy through physical priors. - **2025-01-16: Pipeline Enhancements** - Open-sourced the full [training data pipeline](./docs/prepare_training_data.md) and [MSA pipeline](./docs/msa_template_pipeline.md). - Integrated local [ColabFold-compatible search](./docs/colabfold_compatible_msa.md) for streamlined MSA generation. ## πŸš€ Getting Started ### πŸ›  Quick Installation ```bash pip install protenix ``` ### 🧬 Quick Prediction ```bash # Predict structure using a JSON input protenix pred -i examples/input.json -o ./output -n protenix_base_default_v1.0.0 ``` #### Key Model Descriptions | Model Name | MSA | RNA MSA | Template | Params | Training Data Cutoff | Model Release Date | | :--- | :---: | :---: | :---: | :---: | :---: | :---: | | `protenix_base_default_v1.0.0` | βœ… | βœ… | βœ… | 368 M | 2021-09-30 | 2026-02-05 | | `protenix_base_20250630_v1.0.0` | βœ… | βœ… | βœ… | 368 M | 2025-06-30 | 2026-02-05 | | `protenix_base_default_v0.5.0` | βœ… | ❌ | ❌ | 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). > πŸ’‘ > 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](docs/supported_models.md). For detailed instructions on installation, data preprocessing, inference, and training, please refer to the [Training and Inference Instructions](docs/training_inference_instructions.md). We recommend users refer to [inference_demo.sh](inference_demo.sh) for detailed inference methods and input explanations. ### πŸ“Š Benchmark **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. protenix-v1 model Metrics protenix-v1 model Metrics 2 For detailed benchmark metrics on each dataset, please refer to [docs/model_1.0.0_benchmark.md](docs/model_1.0.0_benchmark.md). ## Citing Protenix If you use Protenix in your research, please cite the following: ``` @article{xiao2026protenixv1, title={Protenix-v1: Toward High-Accuracy Open-Source Biomolecular Structure Prediction}, author={{{Protenix Team}} and Zhang, Yuxuan and Gong, Chengyue and Zhang, Hanyu and Ma, Wenzhi and Liu, Zhenyu and Chen, Xinshi and Guan, Jiaqi and Wang, Lan and Xiao, Wenzhi}, year={2026}, journal={bioRxiv}, publisher={Cold Spring Harbor Laboratory}, doi={10.64898/2026.02.05.703733}, url={https://www.biorxiv.org/content/10.64898/2026.02.05.703733v1}, elocation-id={2026.02.05.703733} } ``` ### πŸ“š 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! πŸ“„ Check out the [Contributing Guide](CONTRIBUTING.md) to get started. βœ… Code Quality: We use `pre-commit` hooks to ensure consistency and code quality. Please install them before making commits: ```bash pip install pre-commit pre-commit install ``` 🐞 Found a bug or have a feature request? [Open an issue](https://github.com/bytedance/Protenix/issues). ## Acknowledgements The implementation of LayerNorm operators refers to both [OneFlow](https://github.com/Oneflow-Inc/oneflow) and [FastFold](https://github.com/hpcaitech/FastFold). We also adopted several [module](protenix/openfold_local/) implementations from [OpenFold](https://github.com/aqlaboratory/openfold), except for [`LayerNorm`](protenix/model/layer_norm/), which is implemented independently. ## Code of Conduct We are committed to fostering a welcoming and inclusive environment. Please review our [Code of Conduct](CODE_OF_CONDUCT.md) 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](https://security.bytedance.com/src) or [vulnerability reporting email](sec@bytedance.com). 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](./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. ## Join Us 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 (*β€œComputational 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** β€” with enhanced support. ### πŸ“ Beijing, China | Type | Expertise | Apply Link | |------------|------------------------------------|------------| | Full-Time | Protein Design Scientist | [Experienced](https://jobs.bytedance.com/society/position/detail/7550992796392982792) | | Full-Time | Computational Biology / Chemistry | [Experienced](https://jobs.bytedance.com/society/position/detail/7505998274429421842), [New Grad](https://job.toutiao.com/s/HGwWBs1UGR4) | | Full-Time | Machine Learning | [Experienced](https://jobs.bytedance.com/society/position/detail/7505999453133015314), [New Grad](https://job.toutiao.com/s/upy82CljXlY) | | Internship | Computational Biology / Chemistry | [Internship](https://job.toutiao.com/s/Wr3yig1Wet4) | | Internship | Machine Learning | [Internship](https://job.toutiao.com/s/w2GQQDfQUkc) | ### πŸ“ Seattle, US | Type | Expertise | Apply Link | |------------|------------------------------------|------------| | Full-Time | Computational Biology / Chemistry | [Experienced](https://jobs.bytedance.com/en/position/7270666468370614585/detail), [New Grad](https://job.toutiao.com/s/iH00nSEvrFo) | | Full-Time | Machine Learning | [Experienced](https://jobs.bytedance.com/en/position/7270665658072926521/detail), [New Grad](https://job.toutiao.com/s/dmU_fbEHGOw) | | Internship | Computational Biology / Chemistry | [Internship](https://job.toutiao.com/s/aiCZz0kJexs) | | Internship | Machine Learning | [Internship](https://job.toutiao.com/s/DiGnn5l1QpQ) |