README.md

EGNN - Pytorch (wip)

Implementation of E(n)-Equivariant Graph Neural Networks, in Pytorch. May be eventually used for Alphafold2 replication. This technique went for simple invariant features, and ended up beating out all previous methods (including SE3 Transformer and Lie Conv) in both accuracy and performance.

Install

$ pip install egnn-pytorch

Usage

import torch
from egnn_pytorch import EGNN

layer1 = EGNN(dim = 512)
layer2 = EGNN(dim = 512)

feats = torch.randn(1, 16, 512)
coors = torch.randn(1, 16, 3)

feats, coors = layer1(feats, coors)
feats, coors = layer2(feats, coors) # (1, 16, 512), (1, 16, 3)

With edges

import torch
from egnn_pytorch import EGNN

layer1 = EGNN(dim = 512, edge_dim = 4)
layer2 = EGNN(dim = 512, edge_dim = 4)

feats = torch.randn(1, 16, 512)
coors = torch.randn(1, 16, 3)
edges = torch.randn(1, 16, 16, 4)

feats, coors = layer1(feats, coors, edges)
feats, coors = layer2(feats, coors, edges) # (1, 16, 512), (1, 16, 3)

Todo

• masking
• add integration with pytorch geometric
• add tests for se3 equivariance
• add an EGAT (attention flavored variant)

Citations

@misc{satorras2021en,
    title 	= {E(n) Equivariant Graph Neural Networks}, 
    author 	= {Victor Garcia Satorras and Emiel Hoogeboom and Max Welling},
    year 	= {2021},
    eprint 	= {2102.09844},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG}
}