[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-71999":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":23,"hasPages":23,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":26,"readmeContent":27,"aiSummary":28,"trendingCount":16,"starSnapshotCount":16,"syncStatus":29,"lastSyncTime":30,"discoverSource":31},71999,"alphafold3","google-deepmind\u002Falphafold3","google-deepmind","AlphaFold 3 inference pipeline.","",null,"Python",8193,1254,83,15,0,52,79,219,156,40.3,"Other",false,"main",[],"2026-06-12 02:02:57","![header](docs\u002Fheader.jpg)\n\n# AlphaFold 3\n\nThis package provides an implementation of the inference pipeline of AlphaFold\n3. See below for how to access the model parameters. You may only use AlphaFold\n3 model parameters if received directly from Google. Use is subject to these\n[terms of use](https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Falphafold3\u002Fblob\u002Fmain\u002FWEIGHTS_TERMS_OF_USE.md).\n\nAny publication that discloses findings arising from using this source code, the\nmodel parameters or outputs produced by those should [cite](#citing-this-work)\nthe\n[Accurate structure prediction of biomolecular interactions with AlphaFold 3](https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41586-024-07487-w)\npaper.\n\nPlease also refer to the Supplementary Information for a detailed description of\nthe method.\n\nAlphaFold 3 is also available at\n[alphafoldserver.com](https:\u002F\u002Falphafoldserver.com) for non-commercial use,\nthough with a more limited set of ligands and covalent modifications.\n\nIf you have any questions, please contact the AlphaFold team at\n[alphafold@google.com](mailto:alphafold@google.com).\n\n## Obtaining Model Parameters\n\nThis repository contains all necessary code for AlphaFold 3 inference. To\nrequest access to the AlphaFold 3 model parameters, please complete\n[this form](https:\u002F\u002Fforms.gle\u002FsvvpY4u2jsHEwWYS6). Access will be granted at\nGoogle DeepMind’s sole discretion. We will aim to respond to requests within 2–3\nbusiness days. You may only use AlphaFold 3 model parameters if received\ndirectly from Google. Use is subject to these\n[terms of use](https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Falphafold3\u002Fblob\u002Fmain\u002FWEIGHTS_TERMS_OF_USE.md).\n\n## Installation and Running Your First Prediction\n\nSee the [installation documentation](docs\u002Finstallation.md).\n\nOnce you have installed AlphaFold 3, you can test your setup using e.g. the\nfollowing input JSON file named `fold_input.json`:\n\n```json\n{\n  \"name\": \"2PV7\",\n  \"sequences\": [\n    {\n      \"protein\": {\n        \"id\": [\"A\", \"B\"],\n        \"sequence\": \"GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG\"\n      }\n    }\n  ],\n  \"modelSeeds\": [1],\n  \"dialect\": \"alphafold3\",\n  \"version\": 1\n}\n```\n\nYou can then run AlphaFold 3 using the following command:\n\n```\ndocker run -it \\\n    --volume $HOME\u002Faf_input:\u002Froot\u002Faf_input \\\n    --volume $HOME\u002Faf_output:\u002Froot\u002Faf_output \\\n    --volume \u003CMODEL_PARAMETERS_DIR>:\u002Froot\u002Fmodels \\\n    --volume \u003CDATABASES_DIR>:\u002Froot\u002Fpublic_databases \\\n    --gpus all \\\n    alphafold3 \\\n    python run_alphafold.py \\\n    --json_path=\u002Froot\u002Faf_input\u002Ffold_input.json \\\n    --model_dir=\u002Froot\u002Fmodels \\\n    --output_dir=\u002Froot\u002Faf_output\n```\n\nThere are various flags that you can pass to the `run_alphafold.py` command, to\nlist them all run `python run_alphafold.py --help`. Two fundamental flags that\ncontrol which parts AlphaFold 3 will run are:\n\n*   `--run_data_pipeline` (defaults to `true`): whether to run the data\n    pipeline, i.e. genetic and template search. This part is CPU-only, time\n    consuming and could be run on a machine without a GPU.\n*   `--run_inference` (defaults to `true`): whether to run the inference. This\n    part requires a GPU.\n\n## AlphaFold 3 Input\n\nSee the [input documentation](docs\u002Finput.md).\n\n## AlphaFold 3 Output\n\nSee the [output documentation](docs\u002Foutput.md).\n\n## Performance\n\nSee the [performance documentation](docs\u002Fperformance.md).\n\n## Known Issues\n\nKnown issues are documented in the\n[known issues documentation](docs\u002Fknown_issues.md).\n\nPlease\n[create an issue](https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Falphafold3\u002Fissues\u002Fnew\u002Fchoose)\nif it is not already listed in [Known Issues](docs\u002Fknown_issues.md) or in the\n[issues tracker](https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Falphafold3\u002Fissues).\n\n## Citing This Work\n\nAny publication that discloses findings arising from using this source code, the\nmodel parameters or outputs produced by those should cite:\n\n```bibtex\n@article{Abramson2024,\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 Bodenstein, Sebastian W. and Evans, David A. and Hung, Chia-Chun and O’Neill, Michael and Reiman, David and Tunyasuvunakool, Kathryn and Wu, Zachary and Žemgulytė, Akvilė and Arvaniti, Eirini and Beattie, Charles and Bertolli, Ottavia and Bridgland, Alex and Cherepanov, Alexey and Congreve, Miles and Cowen-Rivers, Alexander I. and Cowie, Andrew and Figurnov, Michael and Fuchs, Fabian B. and Gladman, Hannah and Jain, Rishub and Khan, Yousuf A. and Low, Caroline M. R. and Perlin, Kuba and Potapenko, Anna and Savy, Pascal and Singh, Sukhdeep and Stecula, Adrian and Thillaisundaram, Ashok and Tong, Catherine and Yakneen, Sergei and Zhong, Ellen D. and Zielinski, Michal and Žídek, Augustin and Bapst, Victor and Kohli, Pushmeet and Jaderberg, Max and Hassabis, Demis and Jumper, John M.},\n  journal = {Nature},\n  title   = {Accurate structure prediction of biomolecular interactions with AlphaFold 3},\n  year    = {2024},\n  volume  = {630},\n  number  = {8016},\n  pages   = {493–-500},\n  doi     = {10.1038\u002Fs41586-024-07487-w}\n}\n```\n\n## Acknowledgements\n\nAlphaFold 3's release was made possible by the invaluable contributions of the\nfollowing people:\n\nAndrew Cowie, Bella Hansen, Charlie Beattie, Chris Jones, Grace Margand,\nJacob Kelly, James Spencer, Josh Abramson, Kathryn Tunyasuvunakool, Kuba Perlin,\nLindsay Willmore, Max Bileschi, Molly Beck, Oleg Kovalevskiy,\nSebastian Bodenstein, Sukhdeep Singh, Tim Green, Toby Sargeant, Uchechi Okereke,\nYotam Doron, and Augustin Žídek (engineering lead).\n\nWe also extend our gratitude to our collaborators at Google and Isomorphic Labs.\n\nAlphaFold 3 uses the following separate libraries and packages:\n\n*   [abseil-cpp](https:\u002F\u002Fgithub.com\u002Fabseil\u002Fabseil-cpp) and\n    [abseil-py](https:\u002F\u002Fgithub.com\u002Fabseil\u002Fabseil-py)\n*   [Docker](https:\u002F\u002Fwww.docker.com)\n*   [DSSP](https:\u002F\u002Fgithub.com\u002FPDB-REDO\u002Fdssp)\n*   [HMMER Suite](https:\u002F\u002Fgithub.com\u002FEddyRivasLab\u002Fhmmer)\n*   [Haiku](https:\u002F\u002Fgithub.com\u002Fdeepmind\u002Fdm-haiku)\n*   [JAX](https:\u002F\u002Fgithub.com\u002Fjax-ml\u002Fjax\u002F)\n*   [libcifpp](https:\u002F\u002Fgithub.com\u002Fpdb-redo\u002Flibcifpp)\n*   [NumPy](https:\u002F\u002Fgithub.com\u002Fnumpy\u002Fnumpy)\n*   [pybind11](https:\u002F\u002Fgithub.com\u002Fpybind\u002Fpybind11) and\n    [pybind11_abseil](https:\u002F\u002Fgithub.com\u002Fpybind\u002Fpybind11_abseil)\n*   [RDKit](https:\u002F\u002Fgithub.com\u002Frdkit\u002Frdkit)\n*   [Tokamax](https:\u002F\u002Fgithub.com\u002Fopenxla\u002Ftokamax)\n*   [tqdm](https:\u002F\u002Fgithub.com\u002Ftqdm\u002Ftqdm)\n\nWe thank all their contributors and maintainers!\n\n## Get in Touch\n\nIf you have any questions not covered in this overview, please contact the\nAlphaFold team at alphafold@google.com.\n\nWe would love to hear your feedback and understand how AlphaFold 3 has been\nuseful in your research. Share your stories with us at\n[alphafold@google.com](mailto:alphafold@google.com).\n\n## Licence and Disclaimer\n\nThis is not an officially supported Google product.\n\nCopyright 2024 DeepMind Technologies Limited.\n\n### AlphaFold 3 Source Code and Model Parameters\n\nThe AlphaFold 3 source code is licensed under the Creative Commons\nAttribution-Non-Commercial ShareAlike International License, Version 4.0\n(CC-BY-NC-SA 4.0) (the \"License\"); you may not use this file except in\ncompliance with the License. You may obtain a copy of the License at\n[https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Falphafold3\u002Fblob\u002Fmain\u002FLICENSE](https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Falphafold3\u002Fblob\u002Fmain\u002FLICENSE).\n\nThe AlphaFold 3 model parameters are made available under the\n[AlphaFold 3 Model Parameters Terms of Use](https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Falphafold3\u002Fblob\u002Fmain\u002FWEIGHTS_TERMS_OF_USE.md)\n(the \"Terms\"); you may not use these except in compliance with the Terms. You\nmay obtain a copy of the Terms at\n[https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Falphafold3\u002Fblob\u002Fmain\u002FWEIGHTS_TERMS_OF_USE.md](https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Falphafold3\u002Fblob\u002Fmain\u002FWEIGHTS_TERMS_OF_USE.md).\n\nUnless required by applicable law, AlphaFold 3 and its output are distributed on\nan \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express\nor implied. You are solely responsible for determining the appropriateness of\nusing AlphaFold 3, or using or distributing its source code or output, and\nassume any and all risks associated with such use or distribution and your\nexercise of rights and obligations under the relevant terms. Output are\npredictions with varying levels of confidence and should be interpreted\ncarefully. Use discretion before relying on, publishing, downloading or\notherwise using the AlphaFold 3 Assets.\n\nAlphaFold 3 and its output are for theoretical modeling only. They are not\nintended, validated, or approved for clinical use. You should not use the\nAlphaFold 3 or its output for clinical purposes or rely on them for medical or\nother professional advice. Any content regarding those topics is provided for\ninformational purposes only and is not a substitute for advice from a qualified\nprofessional. See the relevant terms for the specific language governing\npermissions and limitations under the terms.\n\n### Third-party Software\n\nUse of the third-party software, libraries or code referred to in the\n[Acknowledgements](#acknowledgements) section above may be governed by separate\nterms and conditions or license provisions. Your use of the third-party\nsoftware, libraries or code is subject to any such terms and you should check\nthat you can comply with any applicable restrictions or terms and conditions\nbefore use.\n\n### Mirrored and Reference Databases\n\nThe following databases have been: (1) mirrored by Google DeepMind; and (2) in\npart, included with the inference code package for testing purposes, and are\navailable with reference to the following:\n\n*   [BFD](https:\u002F\u002Fbfd.mmseqs.com\u002F) (modified), by Steinegger M. and Söding J.,\n    modified by Google DeepMind, available under a\n    [Creative Commons Attribution 4.0 International License](https:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby\u002F4.0\u002Fdeed.en).\n    See the Methods section of the\n    [AlphaFold proteome paper](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41586-021-03828-1)\n    for details.\n*   [PDB](https:\u002F\u002Fwwpdb.org) (unmodified), by H.M. Berman et al., available free\n    of all copyright restrictions and made fully and freely available for both\n    non-commercial and commercial use under\n    [CC0 1.0 Universal (CC0 1.0) Public Domain Dedication](https:\u002F\u002Fcreativecommons.org\u002Fpublicdomain\u002Fzero\u002F1.0\u002F).\n*   [MGnify: v2022\\_05](https:\u002F\u002Fftp.ebi.ac.uk\u002Fpub\u002Fdatabases\u002Fmetagenomics\u002Fpeptide_database\u002F2022_05\u002FREADME.txt)\n    (unmodified), by Mitchell AL et al., available free of all copyright\n    restrictions and made fully and freely available for both non-commercial and\n    commercial use under\n    [CC0 1.0 Universal (CC0 1.0) Public Domain Dedication](https:\u002F\u002Fcreativecommons.org\u002Fpublicdomain\u002Fzero\u002F1.0\u002F).\n*   [UniProt: 2021\\_04](https:\u002F\u002Fwww.uniprot.org\u002F) (unmodified), by The UniProt\n    Consortium, available under a\n    [Creative Commons Attribution 4.0 International License](https:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby\u002F4.0\u002Fdeed.en).\n*   [UniRef90: 2022\\_05](https:\u002F\u002Fwww.uniprot.org\u002F) (unmodified) by The UniProt\n    Consortium, available under a\n    [Creative Commons Attribution 4.0 International License](https:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby\u002F4.0\u002Fdeed.en).\n*   [NT: 2023\\_02\\_23](https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fnucleotide\u002F) (modified) See\n    the Supplementary Information of the\n    [AlphaFold 3 paper](https:\u002F\u002Fnature.com\u002Farticles\u002Fs41586-024-07487-w) for\n    details.\n*   [RFam: 14\\_4](https:\u002F\u002Frfam.org\u002F) (modified), by I. Kalvari et al., available\n    free of all copyright restrictions and made fully and freely available for\n    both non-commercial and commercial use under\n    [CC0 1.0 Universal (CC0 1.0) Public Domain Dedication](https:\u002F\u002Fcreativecommons.org\u002Fpublicdomain\u002Fzero\u002F1.0\u002F).\n    See the Supplementary Information of the\n    [AlphaFold 3 paper](https:\u002F\u002Fnature.com\u002Farticles\u002Fs41586-024-07487-w) for\n    details.\n*   [RNACentral: 21\\_0](https:\u002F\u002Frnacentral.org\u002F) (modified), by The RNAcentral\n    Consortium available free of all copyright restrictions and made fully and\n    freely available for both non-commercial and commercial use under\n    [CC0 1.0 Universal (CC0 1.0) Public Domain Dedication](https:\u002F\u002Fcreativecommons.org\u002Fpublicdomain\u002Fzero\u002F1.0\u002F).\n    See the Supplementary Information of the\n    [AlphaFold 3 paper](https:\u002F\u002Fnature.com\u002Farticles\u002Fs41586-024-07487-w) for\n    details.\n","AlphaFold 3 是一个用于蛋白质结构预测的推理管道实现。它基于深度学习技术，能够根据氨基酸序列准确预测蛋白质的三维结构。项目使用Python编写，支持通过Docker容器运行，并需要从Google获取模型参数后才能使用。适用于生物学研究、药物开发等领域中对蛋白质结构有高精度预测需求的场景。请注意，模型参数的使用权需遵循特定条款，并且只能直接从Google获得。",2,"2026-06-11 03:39:53","high_star"]