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(Low effort)","Info: topLevel permissions set to 'read-all': .github/workflows/scorecard.yml:18","Info: no jobLevel write permissions found"],"documentation":{"short":"Determines if the project's workflows follow the principle of least privilege.","url":"https://github.com/ossf/scorecard/blob/49c0eed3a423f00c872b5c3c9f1bbca9e8aae799/docs/checks.md#token-permissions"}},{"name":"Vulnerabilities","score":7,"reason":"3 existing vulnerabilities detected","details":["Warn: Project is vulnerable to: GHSA-mr82-8j83-vxmv","Warn: Project is vulnerable to: PYSEC-2023-102","Warn: Project is vulnerable to: GHSA-887c-mr87-cxwp"],"documentation":{"short":"Determines if the project has open, known unfixed vulnerabilities.","url":"https://github.com/ossf/scorecard/blob/49c0eed3a423f00c872b5c3c9f1bbca9e8aae799/docs/checks.md#vulnerabilities"}}]},"last_synced_at":"2025-09-09T09:24:54.441Z","repository_id":261824263,"created_at":"2025-09-09T09:24:54.453Z","updated_at":"2025-09-09T09:24:54.453Z"},"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31293493,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-01T21:15:39.731Z","status":"ssl_error","status_checked_at":"2026-04-01T21:15:34.046Z","response_time":53,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai4science","research"],"created_at":"2024-11-15T13:15:10.779Z","updated_at":"2026-04-02T00:42:00.990Z","avatar_url":"https://github.com/bytedance.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Protenix: Protein + X\n\n\n\u003e 📣📣📣 **We're hiring!** \\\n\u003e Positions in **_Beijing_** 🇨🇳 and **_Seattle_** 🇺🇸 \\\n\u003e Interested in machine learning, computational chemistry/biology, structural biology, or drug discovery?  Join us to build cutting-edge AI for biology!\n\\\n\u003e 👉 [**Join us »**](#join-us)\n\n\n\n\u003cdiv align=\"center\" style=\"margin: 20px 0;\"\u003e\n  \u003cspan style=\"margin: 0 10px;\"\u003e⚡ \u003ca href=\"https://protenix-server.com\"\u003eProtenix Web Server\u003c/a\u003e\u003c/span\u003e\n  \u0026bull; \u003cspan style=\"margin: 0 10px;\"\u003e📄 \u003ca href=\"docs/PTX_V1_Technical_Report_202602042356.pdf\"\u003eTechnical Report\u003c/a\u003e\u003c/span\u003e\n\u003c/div\u003e\n\n\u003cdiv align=\"center\"\u003e\n\n[![Twitter](https://img.shields.io/badge/Twitter-Follow-blue?logo=x)](https://x.com/ai4s_protenix)\n[![Slack](https://img.shields.io/badge/Slack-Join-yellow?logo=slack)](https://join.slack.com/t/protenixworkspace/shared_invite/zt-3drypwagk-zRnDF2VtOQhpWJqMrIveMw)\n[![Wechat](https://img.shields.io/badge/Wechat-Join-brightgreen?logo=wechat)](https://github.com/bytedance/Protenix/issues/52)\n[![Email](https://img.shields.io/badge/Email-Contact-lightgrey?logo=gmail)](#contact-us)\n\u003c/div\u003e\n\nWe’re excited to introduce **Protenix** — Toward High-Accuracy Open-Source Biomolecular Structure Prediction.\n\nProtenix 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.\n\n\u003cimg src=\"assets/protenix_predictions.gif\" style=\"width: 100%; height: auto;\" alt=\"Protenix predictions\"\u003e\n\n## 🌟 Related Projects\n- **[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 — 2–6× higher than prior SOTA methods such as AlphaProteo and RFdiffusion. The framework is freely accessible via the Protenix Server.\n\n- **[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.\n\n- **[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.\n\n## 🎉 Latest Updates\n- **2026-02-05: Protenix-v1 Released** 💪 [[Technical Report](docs/PTX_V1_Technical_Report_202602042356.pdf)]\n  - Supported Template/RNA MSA features and improved training dynamics, along with further Inference-time model performance enhancements.\n- **2025-11-05: Protenix-v0.7.0 Released** 🚀\n  - Introduced advanced diffusion inference optimizations: Shared variable caching, efficient kernel fusion, and TF32 acceleration. See our [performance analysis](./assets/inference_time_vs_ntoken.png).\n- **2025-07-17: Protenix-Mini \u0026 Constraint Features**\n  - Released lightweight model variants ([Protenix-Mini](https://arxiv.org/abs/2507.11839)) that drastically reduce inference costs with minimal accuracy loss.\n  - Added support for [atom-level contact and pocket constraints](docs/infer_json_format.md#constraint), enhancing prediction accuracy through physical priors.\n- **2025-01-16: Pipeline Enhancements**\n  - Open-sourced the full [training data pipeline](./docs/prepare_training_data.md) and [MSA pipeline](./docs/msa_template_pipeline.md).\n  - Integrated local [ColabFold-compatible search](./docs/colabfold_compatible_msa.md) for streamlined MSA generation.\n\n\n## 🚀 Getting Started\n\n### 🛠 Quick Installation\n\n```bash\npip install protenix\n```\n\n### 🧬 Quick Prediction\n\n```bash\n# Predict structure using a JSON input\nprotenix pred -i examples/input.json -o ./output -n protenix_base_default_v1.0.0\n```\n\n#### Key Model Descriptions\n| Model Name | MSA | RNA MSA | Template | Params | Training Data Cutoff | Model Release Date |\n| :--- | :---: | :---: | :---: | :---: | :---: | :---: |\n| `protenix_base_default_v1.0.0` | ✅ | ✅ | ✅ | 368 M | 2021-09-30 | 2026-02-05 |\n| `protenix_base_20250630_v1.0.0` | ✅ | ✅ | ✅ | 368 M | 2025-06-30 | 2026-02-05 |\n| `protenix_base_default_v0.5.0` | ✅ | ❌ | ❌ | 368 M | 2021-09-30 | 2025-05-30 |\n\n- **protenix_base_default_v1.0.0**: Default model, trained with a data cutoff aligned with AlphaFold3 (2021-09-30).\n  \u003e 💡\n  \u003e This is the **highly recommended** model for conducting fair, rigorous public benchmarks and comparative studies against other state-of-the-art methods.\n- **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.\n- **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.\n\nFor a complete list of supported models, please refer to [Supported Models](docs/supported_models.md).\n\nFor 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.\n\n\n### 📊 Benchmark\n\n**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.\n\n\u003cimg src=\"./assets/protenix_base_default_v1.0.0_metrics.png\" style=\"width: 100%; height: auto;\" alt=\"protenix-v1 model Metrics\"\u003e\n\n\u003cimg src=\"./assets/protenix_base_default_v1.0.0_metrics2.png\" style=\"width: 100%; height: auto;\" alt=\"protenix-v1 model Metrics 2\"\u003e\n\nFor detailed benchmark metrics on each dataset, please refer to [docs/model_1.0.0_benchmark.md](docs/model_1.0.0_benchmark.md).\n\n## Citing Protenix\n\nIf you use Protenix in your research, please cite the following:\n\n```\n@article {Zhang2026.02.05.703733,\n\tauthor = {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},\n\ttitle = {Protenix-v1: Toward High-Accuracy Open-Source Biomolecular Structure Prediction},\n\telocation-id = {2026.02.05.703733},\n\tyear = {2026},\n\tdoi = {10.64898/2026.02.05.703733},\n\tpublisher = {Cold Spring Harbor Laboratory},\n\tURL = {https://www.biorxiv.org/content/early/2026/02/22/2026.02.05.703733.1},\n\teprint = {https://www.biorxiv.org/content/early/2026/02/22/2026.02.05.703733.1.full.pdf},\n\tjournal = {bioRxiv}\n}\n```\n\n### 📚 Citing Related Work\nProtenix 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:\n```\n@article{abramson2024accurate,\n  title={Accurate structure prediction of biomolecular interactions with AlphaFold 3},\n  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},\n  journal={Nature},\n  volume={630},\n  number={8016},\n  pages={493--500},\n  year={2024},\n  publisher={Nature Publishing Group UK London}\n}\n@article{ahdritz2024openfold,\n  title={OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization},\n  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},\n  journal={Nature Methods},\n  volume={21},\n  number={8},\n  pages={1514--1524},\n  year={2024},\n  publisher={Nature Publishing Group US New York}\n}\n@article{mirdita2022colabfold,\n  title={ColabFold: making protein folding accessible to all},\n  author={Mirdita, Milot and Sch{\\\"u}tze, Konstantin and Moriwaki, Yoshitaka and Heo, Lim and Ovchinnikov, Sergey and Steinegger, Martin},\n  journal={Nature methods},\n  volume={19},\n  number={6},\n  pages={679--682},\n  year={2022},\n  publisher={Nature Publishing Group US New York}\n}\n```\n\n## Contributing to Protenix\n\nWe welcome contributions from the community to help improve Protenix!\n\n📄 Check out the [Contributing Guide](CONTRIBUTING.md) to get started.\n\n✅ Code Quality: \nWe use `pre-commit` hooks to ensure consistency and code quality. Please install them before making commits:\n\n```bash\npip install pre-commit\npre-commit install\n```\n\n🐞 Found a bug or have a feature request? [Open an issue](https://github.com/bytedance/Protenix/issues).\n\n\n\n## Acknowledgements\n\n\nThe implementation of LayerNorm operators refers to both [OneFlow](https://github.com/Oneflow-Inc/oneflow) and [FastFold](https://github.com/hpcaitech/FastFold).\nWe 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.\n\n\n## Code of Conduct\n\nWe are committed to fostering a welcoming and inclusive environment.\nPlease review our [Code of Conduct](CODE_OF_CONDUCT.md) for guidelines on how to participate respectfully.\n\n\n## Security\n\nIf you discover a potential security issue in this project, or think you may\nhave 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).\n\nPlease do **not** create a public GitHub issue.\n\n## License\n\nThe 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.\n\n## Contact Us\n\nWe 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.\n\n## Join Us\n\nWe'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. \n\nOutstanding applicants will be considered for **ByteDance’s Top Seed Talent Program** — with enhanced support.\n\n\n### 📍 Beijing, China\n| Type       | Expertise                          | Apply Link |\n|------------|------------------------------------|------------|\n| Full-Time  | Protein Design Scientist       | [Experienced](https://jobs.bytedance.com/society/position/detail/7550992796392982792) |\n| Full-Time  | Computational Biology / Chemistry       | [Experienced](https://jobs.bytedance.com/society/position/detail/7505998274429421842), [New Grad](https://job.toutiao.com/s/HGwWBs1UGR4) |\n| Full-Time  | Machine Learning                   | [Experienced](https://jobs.bytedance.com/society/position/detail/7505999453133015314), [New Grad](https://job.toutiao.com/s/upy82CljXlY) |\n| Internship | Computational Biology / Chemistry       | [Internship](https://job.toutiao.com/s/Wr3yig1Wet4) |\n| Internship | Machine Learning                   | [Internship](https://job.toutiao.com/s/w2GQQDfQUkc) |\n\n\n### 📍 Seattle, US\n\n| Type       | Expertise                          | Apply Link |\n|------------|------------------------------------|------------|\n| Full-Time  | Computational Biology / Chemistry       | [Experienced](https://jobs.bytedance.com/en/position/7270666468370614585/detail), [New Grad](https://job.toutiao.com/s/iH00nSEvrFo) |\n| Full-Time  | Machine Learning                   | [Experienced](https://jobs.bytedance.com/en/position/7270665658072926521/detail), [New Grad](https://job.toutiao.com/s/dmU_fbEHGOw) |\n| Internship | Computational Biology / Chemistry       | [Internship](https://job.toutiao.com/s/aiCZz0kJexs) |\n| Internship | Machine Learning                   | [Internship](https://job.toutiao.com/s/DiGnn5l1QpQ) |\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbytedance%2Fprotenix","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbytedance%2Fprotenix","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbytedance%2Fprotenix/lists"}