Implementation of Alpha Fold 3 from the paper: "Accurate structure prediction of biomolecular interactions with AlphaFold3" in PyTorch
$pip install alphafold3
import torch
# Define the batch size, number of nodes, and number of features
batch_size = 1
num_nodes = 5
num_features = 64
# Generate random pair representations using torch.randn
# Shape: (batch_size, num_nodes, num_nodes, num_features)
pair_representations = torch.randn(
batch_size, num_nodes, num_nodes, num_features
)
# Generate random single representations using torch.randn
# Shape: (batch_size, num_nodes, num_features)
single_representations = torch.randn(
batch_size, num_nodes, num_features
)
Need review but basically it operates on atomic coordinates.
import torch
from alphafold3.diffusion import GeneticDiffusionModuleBlock
# Create an instance of the GeneticDiffusionModuleBlock
model = GeneticDiffusionModuleBlock(channels=3, training=True)
# Generate random input coordinates
input_coords = torch.randn(10, 100, 100, 3)
# Generate random ground truth coordinates
ground_truth = torch.randn(10, 100, 100, 3)
# Pass the input coordinates and ground truth coordinates through the model
output_coords, loss = model(input_coords, ground_truth)
# Print the output coordinates
print(output_coords)
# Print the loss value
print(loss)
import torch
from alphafold3 import AlphaFold3
# Create random tensors
x = torch.randn(1, 5, 5, 64) # Shape: (batch_size, seq_len, seq_len, dim)
y = torch.randn(1, 5, 64) # Shape: (batch_size, seq_len, dim)
# Initialize AlphaFold3 model
model = AlphaFold3(
dim=64,
seq_len=5,
heads=8,
dim_head=64,
attn_dropout=0.0,
ff_dropout=0.0,
global_column_attn=False,
pair_former_depth=48,
num_diffusion_steps=1000,
diffusion_depth=30,
)
# Forward pass through the model
output = model(x, y)
# Print the shape of the output tensor
print(output.shape)
@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
}
sequences, ligands, ,covalent bonds -> input embedder [3] ->
- Implement Figure A, implement triangle update, transition,
- Impelment Figure B, per token, cond,
- Implement Figure C: Network Chunk,
- Implement confidence module
- Implement Template Module