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https://github.com/alexhernandezgarcia/bioseq-gfn-al

Code for "Biological Sequence Design with GFlowNets", 2022

https://github.com/alexhernandezgarcia/bioseq-gfn-al

Science Score: 10.0%

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    Links to: arxiv.org
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Code for "Biological Sequence Design with GFlowNets", 2022

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  • Host: GitHub
  • Owner: alexhernandezgarcia
  • License: mit
  • Default Branch: master
  • Size: 16.6 KB
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Fork of MJ10/BioSeq-GFN-AL
Created over 3 years ago · Last pushed about 4 years ago

https://github.com/alexhernandezgarcia/bioseq-gfn-al/blob/master/ 

# GFlowNets for Biological Sequence Design

This repo contains code for the paper [Biological Sequence Design with GFlowNets](http://arxiv.org/abs/2203.04115). 

The code has been extracted from an internal repository of the Mila Molecule Discovery project. Original commits are lost here, but the credit goes to [@MJ10](https://github.com/MJ10) and [@bengioe](https://github.com/bengioe).  

## Setup
The code has been tested with Python 3.7 with CUDA 10.2 and CUDNN 8.0.

1. Install design-bench from our fork [`MJ10/design-bench`](https://github.com/MJ10/design-bench). This fork only changes some dependencies and resolves some minor changes to make it compatible with our code. To install clone the repo and run `pip install -e .` in the directory where the repo is cloned.
2. Instal the clamp-common-eval library from [MJ10/clamp-gen-data](https://github.com/MJ10/clamp-gen-data). This library handles the loading of the AMP data as well as oracles. To install clone the repo and run `pip install -r requirements.txt && pip install -e .` in the directory where the repo is cloned.
3. Run `pip install -e requirements.txt` in this directory to install the remaining packages.

## Running the code
`run_amp.py`, `run_gfp.py`, and `run_tfbind.py` are the entry points for the experiments.


Please reach out to Moksh Jain, [mokshjn00@gmail.com](mokshjn00@gmail.com) for any issues, comments, questions or suggestions.

Owner

  • Name: Alex
  • Login: alexhernandezgarcia
  • Kind: user

Postdoc at Mila, Montreal. ML, computer vision, cognitive computational neuroscience, vision. Open Science. he/him/his.

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