https://github.com/murrellgroup/messagepassingipa.jl

https://github.com/murrellgroup/messagepassingipa.jl

Last synced: 5 months ago
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• Host: GitHub
• URL: https://github.com/murrellgroup/messagepassingipa.jl
• Owner: MurrellGroup
• License: mit
• Created: 2023-11-03T21:06:07.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2025-02-19T14:34:52.000Z (over 1 year ago)
• Last Synced: 2025-03-10T22:43:50.126Z (about 1 year ago)
• Language: Julia
• Homepage:
• Size: 41 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# MessagePassingIPA

[![Build Status](https://github.com/MurrellGroup/MessagePassingIPA.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/MurrellGroup/MessagePassingIPA.jl/actions/workflows/CI.yml?query=branch%3Amain)

This package introduces an Invariant Point Attention (IPA) layer coupled with

graph-based message passing, tailored to process structured data by effectively

leveraging both geometric and topological information for superior

representation learning. The operations within this package are designed to

support automatic differentiation and GPU acceleration, ensuring optimal

performance.

For a deeper understanding, you may refer to [the AlphaFold2

paper](https://doi.org/10.1038/s41586-021-03819-2), particularly Algorithm 22 in

the supplementary material.

Jumper, J., Evans, R., Pritzel, A. et al. Highly accurate protein structure

prediction with AlphaFold. Nature 596, 583–589 (2021).

# Usage

```julia

# Load packages

using MessagePassingIPA: RigidTransformation, InvariantPointAttention, rigid_from_3points

using GraphNeuralNetworks: rand_graph

# Initialize an IPA layer

n_dims_s = 32  # the dimension of single representations

n_dims_z = 16  # the diemnsion of pair representations

ipa = InvariantPointAttention(n_dims_s, n_dims_z)

# Generate a random graph and node/edge features

n_nodes = 100

n_edges = 500

g = rand_graph(n_nodes, n_edges)

s = randn(Float32, n_dims_s, n_nodes)

z = randn(Float32, n_dims_z, n_edges)

# Generate random atom coordinates

p = randn(Float32, 3, n_nodes) * 100  # centroid

x1 = p .+ randn(Float32, 3, n_nodes)  # N atoms

x2 = p .+ randn(Float32, 3, n_nodes)  # CA atoms

x3 = p .+ randn(Float32, 3, n_nodes)  # C atoms

rigid = RigidTransformation(rigid_from_3points(x1, x2, x3)...)

# Apply the IPA layer

out = ipa(g, s, z, rigid)

@assert size(out) == (n_dims_s, n_nodes)

```