Skip to content

Latest commit

 

History

History
32 lines (23 loc) · 749 Bytes

README.md

File metadata and controls

32 lines (23 loc) · 749 Bytes
Raw

AMPTrans-lstm

Application of deep generative model discovers novel and diverse functional peptides against microbial resistance

Requirements

matplotlib==3.2.2
scipy==1.5.0
numpy==1.19.5
scikit-learn==0.23.1
tensorflow==2.5.0
progressbar2==3.53.1
modlamp>=4.2.3

LSTM_peptides

Transformer_AA

train:
python train_eval.py

finetune:
python finetuning.py

Cite:

Mao, Jiashun, Shenghui Guan, Yongqing Chen, Amir Zeb, Qingxiang Sun, Ranlan Lu, Jie Dong, Jianmin Wang, and Dongsheng Cao. "Application of a deep generative model produces novel and diverse functional peptides against microbial resistance." Computational and Structural Biotechnology Journal (2022). https://doi.org/10.1016/j.csbj.2022.12.029