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https://github.com/google-deepmind/alphafold3

AlphaFold 3 inference pipeline.
https://github.com/google-deepmind/alphafold3

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AlphaFold 3 inference pipeline.

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# AlphaFold 3



This package provides an implementation of the inference pipeline of AlphaFold

3. See below for how to access the model parameters. You may only use AlphaFold

3 model parameters if received directly from Google. Use is subject to these

[terms of use](https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md).



Any publication that discloses findings arising from using this source code, the

model parameters or outputs produced by those should [cite](#citing-this-work)

the

[Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-07487-w)

paper.



Please also refer to the Supplementary Information for a detailed description of

the method.



AlphaFold 3 is also available at

[alphafoldserver.com](https://alphafoldserver.com) for non-commercial use,

though with a more limited set of ligands and covalent modifications.



If you have any questions, please contact the AlphaFold team at

[[email protected]](mailto:[email protected]).



## Obtaining Model Parameters



This repository contains all necessary code for AlphaFold 3 inference. To

request access to the AlphaFold 3 model parameters, please complete

[this form](https://forms.gle/svvpY4u2jsHEwWYS6). Access will be granted at

Google DeepMind’s sole discretion. We will aim to respond to requests within 2–3

business days. You may only use AlphaFold 3 model parameters if received

directly from Google. Use is subject to these

[terms of use](https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md).



## Installation and Running Your First Prediction



See the [installation documentation](docs/installation.md).



Once you have installed AlphaFold 3, you can test your setup using e.g. the

following input JSON file named `fold_input.json`:



```json

{

  "name": "2PV7",

  "sequences": [

    {

      "protein": {

        "id": ["A", "B"],

        "sequence": "GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG"

      }

    }

  ],

  "modelSeeds": [1],

  "dialect": "alphafold3",

  "version": 1

}

```



You can then run AlphaFold 3 using the following command:



```

docker run -it \

    --volume $HOME/af_input:/root/af_input \

    --volume $HOME/af_output:/root/af_output \

    --volume :/root/models \

    --volume :/root/public_databases \

    --gpus all \

    alphafold3 \

    python run_alphafold.py \

    --json_path=/root/af_input/fold_input.json \

    --model_dir=/root/models \

    --output_dir=/root/af_output

```



There are various flags that you can pass to the `run_alphafold.py` command, to

list them all run `python run_alphafold.py --help`. Two fundamental flags that

control which parts AlphaFold 3 will run are:



*   `--run_data_pipeline` (defaults to `true`): whether to run the data

    pipeline, i.e. genetic and template search. This part is CPU-only, time

    consuming and could be run on a machine without a GPU.

*   `--run_inference` (defaults to `true`): whether to run the inference. This

    part requires a GPU.



## AlphaFold 3 Input



See the [input documentation](docs/input.md).



## AlphaFold 3 Output



See the [output documentation](docs/output.md).



## Performance



See the [performance documentation](docs/performance.md).



## Known Issues



Known issues are documented in the

[known issues documentation](docs/known_issues.md).



Please

[create an issue](https://github.com/google-deepmind/alphafold3/issues/new/choose)

if it is not already listed in [Known Issues](docs/known_issues.md) or in the

[issues tracker](https://github.com/google-deepmind/alphafold3/issues).



## Citing This Work



Any publication that discloses findings arising from using this source code, the

model parameters or outputs produced by those should cite:



```bibtex

@article{Abramson2024,

  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.},

  journal = {Nature},

  title   = {Accurate structure prediction of biomolecular interactions with AlphaFold 3},

  year    = {2024},

  volume  = {630},

  number  = {8016},

  pages   = {493–-500},

  doi     = {10.1038/s41586-024-07487-w}

}

```



## Acknowledgements



AlphaFold 3's release was made possible by the invaluable contributions of the

following people:



Andrew Cowie, Bella Hansen, Charlie Beattie, Chris Jones, Grace Margand,

Jacob Kelly, James Spencer, Josh Abramson, Kathryn Tunyasuvunakool, Kuba Perlin,

Lindsay Willmore, Max Bileschi, Molly Beck, Oleg Kovalevskiy,

Sebastian Bodenstein, Sukhdeep Singh, Tim Green, Toby Sargeant, Uchechi Okereke,

Yotam Doron, and Augustin Žídek (engineering lead).



We also extend our gratitude to our collaborators at Google and Isomorphic Labs.



AlphaFold 3 uses the following separate libraries and packages:



*   [abseil-cpp](https://github.com/abseil/abseil-cpp) and

    [abseil-py](https://github.com/abseil/abseil-py)

*   [Chex](https://github.com/deepmind/chex)

*   [Docker](https://www.docker.com)

*   [DSSP](https://github.com/PDB-REDO/dssp)

*   [HMMER Suite](https://github.com/EddyRivasLab/hmmer)

*   [Haiku](https://github.com/deepmind/dm-haiku)

*   [JAX](https://github.com/jax-ml/jax/)

*   [jax-triton](https://github.com/jax-ml/jax-triton)

*   [jaxtyping](https://github.com/patrick-kidger/jaxtyping)

*   [libcifpp](https://github.com/pdb-redo/libcifpp)

*   [NumPy](https://github.com/numpy/numpy)

*   [pybind11](https://github.com/pybind/pybind11) and

    [pybind11_abseil](https://github.com/pybind/pybind11_abseil)

*   [RDKit](https://github.com/rdkit/rdkit)

*   [Tree](https://github.com/deepmind/tree)

*   [Triton](https://github.com/triton-lang/triton)

*   [tqdm](https://github.com/tqdm/tqdm)



We thank all their contributors and maintainers!



## Get in Touch



If you have any questions not covered in this overview, please contact the

AlphaFold team at [email protected].



We would love to hear your feedback and understand how AlphaFold 3 has been

useful in your research. Share your stories with us at

[[email protected]](mailto:[email protected]).



## Licence and Disclaimer



This is not an officially supported Google product.



Copyright 2024 DeepMind Technologies Limited.



### AlphaFold 3 Source Code and Model Parameters



The AlphaFold 3 source code is licensed under the Creative Commons

Attribution-Non-Commercial ShareAlike International License, Version 4.0

(CC-BY-NC-SA 4.0) (the "License"); you may not use this file except in

compliance with the License. You may obtain a copy of the License at

[https://github.com/google-deepmind/alphafold3/blob/main/LICENSE](https://github.com/google-deepmind/alphafold3/blob/main/LICENSE).



The AlphaFold 3 model parameters are made available under the

[AlphaFold 3 Model Parameters Terms of Use](https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md)

(the "Terms"); you may not use these except in compliance with the Terms. You

may obtain a copy of the Terms at

[https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md](https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md).



Unless required by applicable law, AlphaFold 3 and its output are distributed on

an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express

or implied. You are solely responsible for determining the appropriateness of

using AlphaFold 3, or using or distributing its source code or output, and

assume any and all risks associated with such use or distribution and your

exercise of rights and obligations under the relevant terms. Output are

predictions with varying levels of confidence and should be interpreted

carefully. Use discretion before relying on, publishing, downloading or

otherwise using the AlphaFold 3 Assets.



AlphaFold 3 and its output are for theoretical modeling only. They are not

intended, validated, or approved for clinical use. You should not use the

AlphaFold 3 or its output for clinical purposes or rely on them for medical or

other professional advice. Any content regarding those topics is provided for

informational purposes only and is not a substitute for advice from a qualified

professional. See the relevant terms for the specific language governing

permissions and limitations under the terms.



### Third-party Software



Use of the third-party software, libraries or code referred to in the

[Acknowledgements](#acknowledgements) section above may be governed by separate

terms and conditions or license provisions. Your use of the third-party

software, libraries or code is subject to any such terms and you should check

that you can comply with any applicable restrictions or terms and conditions

before use.



### Mirrored and Reference Databases



The following databases have been: (1) mirrored by Google DeepMind; and (2) in

part, included with the inference code package for testing purposes, and are

available with reference to the following:



*   [BFD](https://bfd.mmseqs.com/) (modified), by Steinegger M. and Söding J.,

    modified by Google DeepMind, available under a

    [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/deed.en).

    See the Methods section of the

    [AlphaFold proteome paper](https://www.nature.com/articles/s41586-021-03828-1)

    for details.

*   [PDB](https://wwpdb.org) (unmodified), by H.M. Berman et al., available free

    of all copyright restrictions and made fully and freely available for both

    non-commercial and commercial use under

    [CC0 1.0 Universal (CC0 1.0) Public Domain Dedication](https://creativecommons.org/publicdomain/zero/1.0/).

*   [MGnify: v2022\_05](https://ftp.ebi.ac.uk/pub/databases/metagenomics/peptide_database/2022_05/README.txt)

    (unmodified), by Mitchell AL et al., available free of all copyright

    restrictions and made fully and freely available for both non-commercial and

    commercial use under

    [CC0 1.0 Universal (CC0 1.0) Public Domain Dedication](https://creativecommons.org/publicdomain/zero/1.0/).

*   [UniProt: 2021\_04](https://www.uniprot.org/) (unmodified), by The UniProt

    Consortium, available under a

    [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/deed.en).

*   [UniRef90: 2022\_05](https://www.uniprot.org/) (unmodified) by The UniProt

    Consortium, available under a

    [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/deed.en).

*   [NT: 2023\_02\_23](https://www.ncbi.nlm.nih.gov/nucleotide/) (modified) See

    the Supplementary Information of the

    [AlphaFold 3 paper](https://nature.com/articles/s41586-024-07487-w) for

    details.

*   [RFam: 14\_4](https://rfam.org/) (modified), by I. Kalvari et al., available

    free of all copyright restrictions and made fully and freely available for

    both non-commercial and commercial use under

    [CC0 1.0 Universal (CC0 1.0) Public Domain Dedication](https://creativecommons.org/publicdomain/zero/1.0/).

    See the Supplementary Information of the

    [AlphaFold 3 paper](https://nature.com/articles/s41586-024-07487-w) for

    details.

*   [RNACentral: 21\_0](https://rnacentral.org/) (modified), by The RNAcentral

    Consortium available free of all copyright restrictions and made fully and

    freely available for both non-commercial and commercial use under

    [CC0 1.0 Universal (CC0 1.0) Public Domain Dedication](https://creativecommons.org/publicdomain/zero/1.0/).

    See the Supplementary Information of the

    [AlphaFold 3 paper](https://nature.com/articles/s41586-024-07487-w) for

    details.