This is the implementation of ZeroBind: A protein-specific zero-shot predictor with subgraph matching for drug-target interactions.
ZeroBind is built on Python3, we recommend using a virtual conda environment as enviroment management for the installation of ZeroBind and its dependencies. The virtual environment can be created as follows:
conda create -n your_environment python==3.9
conda activate your_environmentDownload the source code of ZeroBind from GitHub:
git clone https://github.com/myprecioushh/ZeroBind.gitInstall ZeroBind dependencies as following:
conda install pytorch==2.0.0 torchvision==0.15.0 torchaudio==2.0.0 cu102 -c pytorch
conda install pyg==2.3.0 -c pyg
conda install lightning==2.0.1 -c conda-forge or pip install lightning==2.0.1
conda install -c conda-forge rdkit
pip install graphein
pip install fair-esmMultiple hyperparameters can be selected in meta.py.
python metaentry.py --batch_size=4 --num_workers=16 --num_inner_steps=5 --k_query=50After model training starts, the progress bar will be automatically shown on your command line， and the trained model parameters will be saved in "checkpoints" dictory for every epoch.
python metaentry.py --test --num_workers=16 --k_query=50 --checkpoint_path="your_model_path"After predicting with your well trained model, the predicting output will be saved in a "txt" file and the prediction metrics will be shown on your command line.
Online retrieval service and benchmark datasets are in (http://www.csbio.sjtu.edu.cn/bioinf/ZeroBind/).
This project is covered under the Apache 2.0 License.