Install requirements
conda create -n geoab python==3.9
conda activate geoab
pip install -r requirements.txt
Please follow the data preparation scripts in DyMean, which leads the raw data set as
- all_data
- RAbD_H3
- test_processed
_metainfo
part_0.pkl
- train_processed
...
- valid_processed
...
test.json
train.json
valid.json
- SKEMPI
...
The processed data can be downloaded from google drive https://drive.google.com/drive/folders/1pNsoGt0gDIJR9EMmmp2pItjZDuLZI3gg.
After downloading all_data.zip
, unzip it and the processed datasets can be obtained.
Run the following command for training:
# Train GeoAB-refiner
python train_refine.py
# Train GeoAB-Initializer
python train_init.py
# After GeoAB-Initializer is trained, train GeoAB-Designer
python train_design.py
For evaluation, run the following command:
# Evaluate GeoAB-Refiner
python eval.py --eval_dir H3_refine --run 1
# Evaluate GeoAB-Designer
python eval.py --eval_dir H3_design
We give the pretrained model in cdrh3.zip
, which can be downloaded from https://drive.google.com/drive/folders/1pNsoGt0gDIJR9EMmmp2pItjZDuLZI3gg. You can evaluate the results directly using our pretrained models.
For DDG prediction part, our model will be updated through a platform, which will be online soon.
Please cite the paper if the repository or the paper is helpful to you, as the following
@article {lin2024geoab,
author = {Lin, Haitao and Wu, Lirong and Huang, Yufei and Liu, Yunfan and Zhang, Odin and Zhou, Yuanqing and Sun, Rui and Li, Stan Z.},
title = {GeoAB: Towards Realistic Antibody Design and Reliable Affinity Maturation},
year = {2024},
booktitle={International Conference on Machine Learning},
URL = {https://www.biorxiv.org/content/early/2024/05/17/2024.05.15.594274},
eprint = {https://www.biorxiv.org/content/early/2024/05/17/2024.05.15.594274.full.pdf}
}