Implementation for "Graph Denoising Diffusion for Inverse Protein Folding" arxiv link.
To install requirements:
conda env create -f environment.yml
Like denoising-diffusion-pytorch, there is a brief introduction to show how this discrete diffusion work.
import sys
sys.path.append('diffusion')
import torch
from torch_geometric.data import Batch
from diffusion.gradeif import GraDe_IF,EGNN_NET
from dataset_src.generate_graph import prepare_graph
gnn = EGNN_NET(input_feat_dim=input_graph.x.shape[1]+input_graph.extra_x.shape[1],hidden_channels=10,edge_attr_dim=input_graph.edge_attr.shape[1])
diffusion_model = GraDe_IF(gnn)
graph = torch.load('dataset/process/test/3fkf.A.pt')
input_graph = Batch.from_data_list([prepare_graph(graph)])
loss = diffusion_model(input_graph)
loss.backward()
_,sample_seq = diffusion_model.ddim_sample(input_graph) #using structure information generate sequence
More details can be found notebook
- Our codebase for the EGNN models and discrete diffusion builds on EGNN, DiGress. Thanks for open-sourcing!
If you consider our codes and datasets useful, please cite:
@inproceedings{
yi2023graph,
title={Graph Denoising Diffusion for Inverse Protein Folding},
author={Kai Yi and Bingxin Zhou and Yiqing Shen and Pietro Lio and Yu Guang Wang},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=u4YXKKG5dX}
}