AMP-GAN

The GAN model to generate AMPs/ AVPs and other peptides.

Installation

To clone the package and install dependency packages:

git clone https://github.com/lsbnb/amp_gan.git
cd amp_gan
pip3 install -r requirements.txt

How to use

1. Call the library:

#The path of input file
from amp_gan.train import main as train_gan
fasta_path=""
#The folder to save result
outout_root=""
train_gan(fasta_path,outout_root,batch_size=8,step=10,epoch=100)

2. Call the script:

python3 amp_gan/train.py --f $fasta_path -o $outout_root --b 8 --s 10 --e 100

To get more detail of the usage

The detail settings for using train.py would be shown by using the follwoing codes

python3 amp_gan/train.py -h

Examples

There are two example to be used.

1. Jupyter-notebook example is located in example/example.ipynb.
2. Script example is located in example/example.sh.

Reference

If you find AMP-GAN useful, please consider citing: Discovering Novel Antimicrobial Peptides in Generative Adversarial Network

@article {Lin2021.11.22.469634,
	author = {Lin, Tzu-Tang and Yang, Li-Yen and Wang, Ching-Tien and Lai, Ga-Wen and Ko, Chi-Fong and Shih, Yang-Hsin and Chen, Shu-Hwa and Lin, Chung-Yen},
	title = {Discovering Novel Antimicrobial Peptides in Generative Adversarial Network},
	elocation-id = {2021.11.22.469634},
	year = {2021},
	doi = {10.1101/2021.11.22.469634},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2021/11/23/2021.11.22.469634},
	eprint = {https://www.biorxiv.org/content/early/2021/11/23/2021.11.22.469634.full.pdf},
	journal = {bioRxiv}
}