google-deepmind/alphafold3

google-deepmind/alphafold3

Description: AlphaFold 3 inference pipeline.

Language: Python

License: Apache-2.0

Stars: 8196

Forks: 1256

Open issues: 20

Created: 2024-11-11T10:38:11Z

Pushed: 2026-06-09T12:26:49Z

Default branch: main

Fork: no

Archived: no

README: 

AlphaFold 3

This package provides an implementation of the inference pipeline of AlphaFold

1. 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.

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 paper.

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

AlphaFold 3 is also available at 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 [alphafold@google.com](mailto:alphafold@google.com).

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. 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.

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:

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

Citing This Work

Any publication that discloses findings arising from using this source code, the model parameters or outputs produced by those should cite:

@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 and

abseil-py

• Docker
• DSSP
• HMMER Suite
• Haiku
• JAX
• libcifpp
• NumPy

*…