Deep Learning

Implementation of Graph Transformer in Pytorch

Graph Transformer – Pytorch

Implementation of Graph Transformer in Pytorch, for potential use in replicating Alphafold2. This was recently used by both Costa et al and Bakers lab for transforming MSA and pair-wise embedding into 3d coordinates.

Todo

  • add rotary embeddings for injecting adjacency information

Install

$ pip install graph-transformer-pytorch

Usage

import torch

from graph_transformer_pytorch import GraphTransformer


model = GraphTransformer(

    dim = 256,

    depth = 6,

    edge_dim = 512,             # optional - if left out, edge dimensions is assumed to be the same as the node dimensions above

    with_feedforwards = True,   # whether to add a feedforward after each attention layer, suggested by literature to be needed

    gated_residual = True       # to use the gated residual to prevent over-smoothing

)


nodes =


 


 




 


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