Generative Models for Graph-Based Protein Design

Overview

Graph-Based Protein Design

This repo contains code for Generative Models for Graph-Based Protein Design by John Ingraham, Vikas Garg, Regina Barzilay and Tommi Jaakkola, NeurIPS 2019.

Our approach 'designs' protein sequences for target 3D structures via a graph-conditioned, autoregressive language model:

Overview

  • struct2seq/ contains model code
  • experiments/ contains scripts for training and evaluating the model
  • data/ contains scripts for building and processing datasets in the paper

Requirements

  • Python >= 3.0
  • PyTorch >= 1.0
  • Numpy

Citation

@inproceedings{ingraham2019generative,
author = {Ingraham, John and Garg, Vikas K and Barzilay, Regina and Jaakkola, Tommi},
title = {Generative Models for Graph-Based Protein Design},
booktitle = {Advances in Neural Information Processing Systems}
year = {2019}
}
Owner
John Ingraham
Postdoc
John Ingraham
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