Alphafold 3 - Pytorch

Implementation of Alphafold 3 in Pytorch

You can chat with other researchers about this work here

Review of the paper by Sergey

Illustrated guide by Elana P. Simon

Talk by Max Jaderberg

A fork with full Lightning + Hydra support is being maintained by Alex at this repository

Appreciation

Joseph for contributing the Relative Positional Encoding and the Smooth LDDT Loss!
Felipe for contributing Weighted Rigid Align, Express Coordinates In Frame, Compute Alignment Error, and Centre Random Augmentation modules!
Alex for fixing various issues in the transcribed algorithms
Heng for pointing out inconsistencies with the paper and pull requesting the solutions
Heng for finding an issue with the molecular atom indices for the distogram loss
Wei Lu for catching a few erroneous hyperparameters
Alex for the PDB dataset preparation script!
Milot for optimizing the PDB dataset clustering script!
Alex for basically writing the entire gargantuan flow from parsing the PDB all the way to the molecule and atomic inputs for training
Andrei for working on the weighted PDB dataset sampling!
Jimin for submitting a small fix to an issue with the coordinates being passed into WeightedRigidAlign
@xluo233 for contributing the confidence measures, clash penalty ranking, and sample ranking logic!
sj900 for integrating and testing the WeightedPDBSampler within the PDBDataset!
@xluo233 again for contributing the logic for computing the model selection score as well as the unresolved rasa!
Fandi for discovering a few inconsistencies in the elucidated atom diffusion module with the supplementary
Patrick for jaxtyping, Florian for einx, and of course, Alex for einops

Install

$ pip install alphafold3-pytorch

Usage

import torch
from alphafold3_pytorch import Alphafold3

alphafold3 = Alphafold3(
    dim_atom_inputs = 77,
    dim_template_feats = 44
)

# mock inputs

seq_len = 16
molecule_atom_lens = torch.randint(1, 3, (2, seq_len))
atom_seq_len = molecule_atom_lens.sum(dim = -1).amax()

atom_inputs = torch.randn(2, atom_seq_len, 77)
atompair_inputs = torch.randn(2, atom_seq_len, atom_seq_len, 5)

additional_molecule_feats = torch.randint(0, 2, (2, seq_len, 5))
additional_token_feats = torch.randn(2, seq_len, 2)
is_molecule_types = torch.randint(0, 2, (2, seq_len, 5)).bool()
is_molecule_mod = torch.randint(0, 2, (2, seq_len, 4)).bool()
molecule_ids = torch.randint(0, 32, (2, seq_len))

template_feats = torch.randn(2, 2, seq_len, seq_len, 44)
template_mask = torch.ones((2, 2)).bool()

msa = torch.randn(2, 7, seq_len, 64)
msa_mask = torch.ones((2, 7)).bool()

# required for training, but omitted on inference

atom_pos = torch.randn(2, atom_seq_len, 3)
molecule_atom_indices = molecule_atom_lens - 1 # last atom, as an example

distance_labels = torch.randint(0, 37, (2, seq_len, seq_len))
pae_labels = torch.randint(0, 64, (2, seq_len, seq_len))
pde_labels = torch.randint(0, 64, (2, seq_len, seq_len))
plddt_labels = torch.randint(0, 50, (2, seq_len))
resolved_labels = torch.randint(0, 2, (2, seq_len))

# train

loss = alphafold3(
    num_recycling_steps = 2,
    atom_inputs = atom_inputs,
    atompair_inputs = atompair_inputs,
    molecule_ids = molecule_ids,
    molecule_atom_lens = molecule_atom_lens,
    additional_molecule_feats = additional_molecule_feats,
    additional_token_feats = additional_token_feats,
    is_molecule_types = is_molecule_types,
    is_molecule_mod = is_molecule_mod,
    msa = msa,
    msa_mask = msa_mask,
    templates = template_feats,
    template_mask = template_mask,
    atom_pos = atom_pos,
    molecule_atom_indices = molecule_atom_indices,
    distance_labels = distance_labels,
    pae_labels = pae_labels,
    pde_labels = pde_labels,
    plddt_labels = plddt_labels,
    resolved_labels = resolved_labels
)

loss.backward()

# after much training ...

sampled_atom_pos = alphafold3(
    num_recycling_steps = 4,
    num_sample_steps = 16,
    atom_inputs = atom_inputs,
    atompair_inputs = atompair_inputs,
    molecule_ids = molecule_ids,
    molecule_atom_lens = molecule_atom_lens,
    additional_molecule_feats = additional_molecule_feats,
    additional_token_feats = additional_token_feats,
    is_molecule_types = is_molecule_types,
    is_molecule_mod = is_molecule_mod,
    msa = msa,
    msa_mask = msa_mask,
    templates = template_feats,
    template_mask = template_mask
)

sampled_atom_pos.shape # (2, <atom_seqlen>, 3)

An example with molecule level input handling

import torch

from alphafold3_pytorch import (
    Alphafold3,
    Alphafold3Input,
    alphafold3_inputs_to_batched_atom_input
)

contrived_protein = 'AG'

mock_atompos = [
    torch.randn(5, 3),   # alanine has 5 non-hydrogen atoms
    torch.randn(4, 3)    # glycine has 4 non-hydrogen atoms
]

train_alphafold3_input = Alphafold3Input(
    proteins = [contrived_protein],
    atom_pos = mock_atompos
)

eval_alphafold3_input = Alphafold3Input(
    proteins = [contrived_protein]
)

batched_atom_input = alphafold3_inputs_to_batched_atom_input(train_alphafold3_input, atoms_per_window = 27)

# training

alphafold3 = Alphafold3(
    dim_atom_inputs = 3,
    dim_atompair_inputs = 5,
    atoms_per_window = 27,
    dim_template_feats = 44,
    num_dist_bins = 38,
    num_molecule_mods = 0,
    confidence_head_kwargs = dict(
        pairformer_depth = 1
    ),
    template_embedder_kwargs = dict(
        pairformer_stack_depth = 1
    ),
    msa_module_kwargs = dict(
        depth = 1
    ),
    pairformer_stack = dict(
        depth = 2
    ),
    diffusion_module_kwargs = dict(
        atom_encoder_depth = 1,
        token_transformer_depth = 1,
        atom_decoder_depth = 1,
    )
)

loss = alphafold3(**batched_atom_input.dict())
loss.backward()

# sampling

batched_eval_atom_input = alphafold3_inputs_to_batched_atom_input(eval_alphafold3_input, atoms_per_window = 27)

alphafold3.eval()
sampled_atom_pos = alphafold3(**batched_eval_atom_input.dict())

assert sampled_atom_pos.shape == (1, (5 + 4), 3)

Data preparation

PDB dataset curation

To acquire the AlphaFold 3 PDB dataset, first download all first-assembly (and asymmetric unit) complexes in the Protein Data Bank (PDB), and then preprocess them with the script referenced below. The PDB can be downloaded from the RCSB: https://www.wwpdb.org/ftp/pdb-ftp-sites#rcsbpdb. The two Python scripts below (i.e., filter_pdb_{train,val,test}_mmcifs.py and cluster_pdb_{train,val,test}_mmcifs.py) assume you have downloaded the PDB in the mmCIF file format, placing its first-assembly and asymmetric unit mmCIF files at data/pdb_data/unfiltered_assembly_mmcifs/ and data/pdb_data/unfiltered_asym_mmcifs/, respectively.

For reproducibility, we recommend downloading the PDB using AWS snapshots (e.g., 20240101). To do so, refer to AWS's documentation to set up the AWS CLI locally. Alternatively, on the RCSB website, navigate down to "Download Protocols", and follow the download instructions depending on your location.

For example, one can use the following commands to download the PDB as two collections of mmCIF files:

# For `assembly1` complexes, use the PDB's `20240101` AWS snapshot:
aws s3 sync s3://pdbsnapshots/20240101/pub/pdb/data/assemblies/mmCIF/divided/ ./data/pdb_data/unfiltered_assembly_mmcifs
# Or as a fallback, use rsync:
rsync -rlpt -v -z --delete --port=33444 \
rsync.rcsb.org::ftp_data/assemblies/mmCIF/divided/ ./data/pdb_data/unfiltered_assembly_mmcifs/

# For asymmetric unit complexes, also use the PDB's `20240101` AWS snapshot:
aws s3 sync s3://pdbsnapshots/20240101/pub/pdb/data/structures/divided/mmCIF/ ./data/pdb_data/unfiltered_asym_mmcifs
# Or as a fallback, use rsync:
rsync -rlpt -v -z --delete --port=33444 \
rsync.rcsb.org::ftp_data/structures/divided/mmCIF/ ./data/pdb_data/unfiltered_asym_mmcifs/

WARNING: Downloading the PDB can take up to 700GB of space.

NOTE: The PDB hosts all available AWS snapshots here: https://pdbsnapshots.s3.us-west-2.amazonaws.com/index.html.

After downloading, you should have two directories formatted like this: https://files.rcsb.org/pub/pdb/data/assemblies/mmCIF/divided/ & https://files.rcsb.org/pub/pdb/data/structures/divided/mmCIF/

00/
01/
02/
..
zz/

For these directories, unzip all the files:

find ./data/pdb_data/unfiltered_assembly_mmcifs/ -type f -name "*.gz" -exec gzip -d {} \;
find ./data/pdb_data/unfiltered_asym_mmcifs/ -type f -name "*.gz" -exec gzip -d {} \;

Next run the commands

wget -P ./data/ccd_data/ https://files.wwpdb.org/pub/pdb/data/monomers/components.cif.gz
wget -P ./data/ccd_data/ https://files.wwpdb.org/pub/pdb/data/component-models/complete/chem_comp_model.cif.gz

from the project's root directory to download the latest version of the PDB's Chemical Component Dictionary (CCD) and its structural models. Extract each of these files using the following command:

find data/ccd_data/ -type f -name "*.gz" -exec gzip -d {} \;

PDB dataset filtering

Then run the following with pdb_assembly_dir, pdb_asym_dir, ccd_dir, and mmcif_output_dir replaced with the locations of your local copies of the first-assembly PDB, asymmetric unit PDB, CCD, and your desired dataset output directory (i.e., ./data/pdb_data/unfiltered_assembly_mmcifs/, ./data/pdb_data/unfiltered_asym_mmcifs/, ./data/ccd_data/, and ./data/pdb_data/{train,val,test}_mmcifs/).

python scripts/filter_pdb_train_mmcifs.py --mmcif_assembly_dir <pdb_assembly_dir> --mmcif_asym_dir <pdb_asym_dir> --ccd_dir <ccd_dir> --output_dir <mmcif_output_dir>
python scripts/filter_pdb_val_mmcifs.py --mmcif_assembly_dir <pdb_assembly_dir> --mmcif_asym_dir <pdb_asym_dir> --output_dir <mmcif_output_dir>
python scripts/filter_pdb_test_mmcifs.py --mmcif_assembly_dir <pdb_assembly_dir> --mmcif_asym_dir <pdb_asym_dir> --output_dir <mmcif_output_dir>

See the scripts for more options. Each first-assembly mmCIF that successfully passes all processing steps will be written to mmcif_output_dir within a subdirectory named according to the mmCIF's second and third PDB ID characters (e.g. 5c).

PDB dataset clustering

Next, run the following with mmcif_dir and {train,val,test}_clustering_output_dir replaced, respectively, with your local output directory created using the dataset filtering script above and with your desired clustering output directories (i.e., ./data/pdb_data/{train,val,test}_mmcifs/ and ./data/pdb_data/data_caches/{train,val,test}_clusterings/):

python scripts/cluster_pdb_train_mmcifs.py --mmcif_dir <mmcif_dir> --output_dir <train_clustering_output_dir> --clustering_filtered_pdb_dataset
python scripts/cluster_pdb_val_mmcifs.py --mmcif_dir <mmcif_dir> --reference_clustering_dir <train_clustering_output_dir> --output_dir <val_clustering_output_dir> --clustering_filtered_pdb_dataset
python scripts/cluster_pdb_test_mmcifs.py --mmcif_dir <mmcif_dir> --reference_1_clustering_dir <train_clustering_output_dir> --reference_2_clustering_dir <val_clustering_output_dir> --output_dir <test_clustering_output_dir> --clustering_filtered_pdb_dataset

Note: The --clustering_filtered_pdb_dataset flag is recommended when clustering the filtered PDB dataset as curated using the scripts above, as this flag will enable faster runtimes in this context (since filtering leaves each chain's residue IDs 1-based). However, this flag must not be provided when clustering other (i.e., non-PDB) datasets of mmCIF files. Otherwise, interface clustering may be performed incorrectly, as these datasets' mmCIF files may not use strict 1-based residue indexing for each chain.

Note: One can instead download preprocessed (i.e., filtered) mmCIF (train/val/test) files (~25GB, comprising 148k complexes) and chain/interface clustering (train/val/test) files (~3GB) for the PDB's 20240101 AWS snapshot via a shared OneDrive folder. Each of these tar archives should be uncompressed within the data/pdb_data/ directory.

Contributing

At the project root, run

$ sh ./contribute.sh

Then, add your module to alphafold3_pytorch/alphafold3.py, add your tests to tests/test_af3.py, and submit a pull request. You can run the tests locally with

$ pytest tests/

Docker Image

The included Dockerfile contains the required dependencies to run the package and to train/inference using PyTorch with GPUs.

The default base image is pytorch/pytorch:2.3.0-cuda12.1-cudnn8-runtime and installs the latest version of this package from the main GitHub branch.

## Build Docker Container
docker build -t af3 .

Alternatively, use build arguments to rebuild the image with different software versions:

PYTORCH_TAG: Changes the base image and thus builds with different PyTorch, CUDA, and/or cuDNN versions.
GIT_TAG: Changes the tag of this repo to clone and install the package.

For example:

## Use build argument to change versions
docker build --build-arg "PYTORCH_TAG=2.2.1-cuda12.1-cudnn8-devel" --build-arg "GIT_TAG=0.1.15" -t af3 .

Then, run the container with GPUs and mount a local volume (for training) using the following command:

## Run Container
docker run -v .:/data --gpus all -it af3

Citations

@article{Abramson2024-fj,
  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 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 {\v Z}emgulyt{\.e},
              Akvil{\.e} 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
              {\v Z}{\'\i}dek, Augustin and Bapst, Victor and Kohli, Pushmeet
              and Jaderberg, Max and Hassabis, Demis and Jumper, John M",
  journal  = "Nature",
  month    = "May",
  year     =  2024
}

@inproceedings{Darcet2023VisionTN,
    title   = {Vision Transformers Need Registers},
    author  = {Timoth'ee Darcet and Maxime Oquab and Julien Mairal and Piotr Bojanowski},
    year    = {2023},
    url     = {https://api.semanticscholar.org/CorpusID:263134283}
}

@article{Arora2024SimpleLA,
    title   = {Simple linear attention language models balance the recall-throughput tradeoff},
    author  = {Simran Arora and Sabri Eyuboglu and Michael Zhang and Aman Timalsina and Silas Alberti and Dylan Zinsley and James Zou and Atri Rudra and Christopher R'e},
    journal = {ArXiv},
    year    = {2024},
    volume  = {abs/2402.18668},
    url     = {https://api.semanticscholar.org/CorpusID:268063190}
}

@article{Puny2021FrameAF,
    title   = {Frame Averaging for Invariant and Equivariant Network Design},
    author  = {Omri Puny and Matan Atzmon and Heli Ben-Hamu and Edward James Smith and Ishan Misra and Aditya Grover and Yaron Lipman},
    journal = {ArXiv},
    year    = {2021},
    volume  = {abs/2110.03336},
    url     = {https://api.semanticscholar.org/CorpusID:238419638}
}

@article{Duval2023FAENetFA,
    title   = {FAENet: Frame Averaging Equivariant GNN for Materials Modeling},
    author  = {Alexandre Duval and Victor Schmidt and Alex Hernandez Garcia and Santiago Miret and Fragkiskos D. Malliaros and Yoshua Bengio and David Rolnick},
    journal = {ArXiv},
    year    = {2023},
    volume  = {abs/2305.05577},
    url     = {https://api.semanticscholar.org/CorpusID:258564608}
}

@article{Wang2022DeepNetST,
    title   = {DeepNet: Scaling Transformers to 1, 000 Layers},
    author  = {Hongyu Wang and Shuming Ma and Li Dong and Shaohan Huang and Dongdong Zhang and Furu Wei},
    journal = {ArXiv},
    year    = {2022},
    volume  = {abs/2203.00555},
    url     = {https://api.semanticscholar.org/CorpusID:247187905}
}

@inproceedings{Ainslie2023CoLT5FL,
    title   = {CoLT5: Faster Long-Range Transformers with Conditional Computation},
    author  = {Joshua Ainslie and Tao Lei and Michiel de Jong and Santiago Ontan'on and Siddhartha Brahma and Yury Zemlyanskiy and David Uthus and Mandy Guo and James Lee-Thorp and Yi Tay and Yun-Hsuan Sung and Sumit Sanghai},
    year    = {2023}
}

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README.md

README.md

Alphafold 3 - Pytorch

Appreciation

Install

Usage

Data preparation

PDB dataset curation

PDB dataset filtering

PDB dataset clustering

Contributing

Docker Image

Citations

Files

README.md

Latest commit

History

README.md

File metadata and controls

Alphafold 3 - Pytorch

Appreciation

Install

Usage

Data preparation

PDB dataset curation

PDB dataset filtering

PDB dataset clustering

Contributing

Docker Image

Citations