This is the implementation of PGpocket: : A point cloud graph neural network for protein-ligand binding site prediction
PGpocket is built on Python3, we recommend using a virtual conda environment as enviroment management for the installation of PGpocket 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 PGpocket from GitHub:
git clone https://github.com/username/my-project.gitInstall PGpocket dependencies as following:
conda env create -f environment.yamlThe PDB sturcture used in this study can be download from the link https://zenodo.org/records/13283856
Multiple hyperparameters can be selected in main.py.
python main.pyAfter model training starts, the progress bar will be automatically shown on your command line， and the trained model parameters will be saved in "runs" dictory for every epoch.
Model parameters can be found under the runs folder
python predict.pyAfter predicting with your well trained model, the predicting output will be saved in a "npy" file.
This project is covered under the Apache 2.0 License.