Reinforcement learning with LoRA for targeted antimicrobial peptide design

Abstract

This section will be completed after publication.

Model weight and data

Google drive link

AMP classifier
This folder into the AMP-RL folder

MIC predictor
This folder into the AMP-RL folder

Pretrained model ckpt
This folder into the ckpt/Pretrain folder

PeptideAtlas data
This csv file into the data folder

Model Implementation

The model implemented for the pretraining & reinforcement learning in GPT_PeptideAtlas_Pretraining.ipynb and AMP_RL Reinforcement learning.ipynb respectively

# environment setting
$ git clone https://github.com/GIST-CSBL/AMP-RL.git

$ cd AMP-RL

$ conda create -n AMP-RL python=3.9

$ conda activate AMP-RL

$ pip install -r requirements.txt

$ pip install ipykernel

$ python -m ipykernel install --user --name AMP-RL --display_name "AMP-RL"

License

The source code in this repository is licensed under the PolyForm Noncommercial License 1.0.0.

See the LICENSE file for more information.

The trained model weights and any generated data are licensed under the CC-BY-NC-4.0.

See the CC BY-NC-SA 4.0 file for more information.

Contact

Juntae Park (nuwana9876@gm.gist.ac.kr)

Daehun Bae (qoeogns09@gm.gist.ac.kr)

Hojung Nam* (hjnam@gist.ac.kr)

*Corresponding Author