Skip to content

Collinformatics/CleaveNet

Repository files navigation

Installation:

These instructions will walk you through installing the program in the terminal.

  • Requirements:
    • If you are using a Windows OS, you need to install and use WSL.
    • You need to have conda installed.

Clone the GitHub

git clone https://github.com/Collinformatics/CleaveNet

Create conda environment:

  • If you are using MacOS run:

    conda env create -f environment_mac.yml
    
  • If not, run:

     conda env create -f environment.yml
    

Activate the virtual environment:

  conda activate cleavenet

Test GPU activation:

  python testGPU.py

If you are using an NVIDIA GPU, you can monitor GPU usage with:

  watch -n 1 nvidia-smi

Generator:

Training:

  • Train a model that can predice protein substrates.

    Multiple parameters can be adjusted, to print the options run:

    python src/train_generator.py --help
    
  • You can train the generator with:

    python src/train_generator.py --data-path <filepath>
    

About

This is a modified version of CleaveNet, a ML pipeline that can be used for substrate design and activity prediction. The scripts have been altered to allow for training with datasets extracted with COMET. The original CleaveNet scripts are available at:

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Contributors