This repository contains the official code for the paper "AI-based D-amino acid substitution for optimizing antimicrobial peptides to treat multidrug-resistant bacterial infection"

mamba_ssm==2.2.4
numpy==1.26.3
pandas==2.1.4
rdkit==2024.3.5
scikit_learn==1.4.1.post1
scipy==1.13.0
torch==2.2.0
torchmetrics==1.3.1
torchvision==0.17.0

You can install them with pip install -r requirements.txt

main.py can train model with Classification or Regression tasks.

example:

python main.py \
    --q-encoder rn18 \ # Encoder, can be rn18, lstm, gru, mamba, mha
    --channels 16 \ # Encoder channels. Does not work for ResNet18
    --side-enc mamba \ # Side sequence Encoder, only lstm and mamba implemented, only use with rn18 encoder
    --fusion diff \ # Fusion method, can be att, mlp or diff
    --task cls \ # Task, can be cls or reg
    --loss ce \ # Loss, can be ce or mse, some other losses can be found in code
    --batch-size 32 \ # Batch size
    --epochs 50 \ # Epochs
    --gpu 0 \ # GPU index to use, -1 for cpu
# ===CNN only options=== \
    --pcs \ # Enable protease cleavage site dyeing for input pictures
    --resize 768 \ # Resize input pictures, can be 1 or 2 numbers like 768 or 768 512

Corresponding model weight checkpoints will be saved in the subdirectory of run-cls or run-reg, e.g. /run-cls/rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/.

For more arguments, please refer to the code of main.py.

You can use uda.py to train a UDA model. Arguments are the same as main.py, except that you need to specify the case domain with --case r2 or --case {YOUR_PEPTIDE_SEQUENCE}.

Model weights will be saved in the subdirectory of the normal training directory, e.g. /run-cls/rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/uda_r2/

You can simple replace main.py with infer.py in your training command to do inference on test set.

For case study scanning, please use infer_case.py with an additional argument --case r2 or --case {YOUR_PEPTIDE_SEQUENCE}. If you wish to inference with UDA weights, please use --uda argument.

Inference results will be saved in the weights directory in csv format, e.g. /run-cls/rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/preds_test.csv. For wet lab ranking, please refer to wetlab_ranking.py in the wetlab_rank folder.

See here

Please cite the following work if you find this work useful:

@article{zhao2026ai,
  title={AI-Based D-Amino Acid Substitution for Optimizing Antimicrobial Peptides to Treat Multidrug-Resistant Bacterial Infection},
  author={Zhao, Yinuo and Kong, Qingzhou and Gong, Haifan and Li, Lixiang and Fu, Jialu and Wan, Boyao and Wang, Peizhu and Li, Xiaojuan and Wang, Yue and Zhang, Jinghui and others},
  journal={Advanced Science},
  pages={e18522},
  year={2026},
  publisher={Wiley Online Library}
}