prokia / tankbind

Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction-Galixir Tenchnologies

Paper on arXiv

TankBind

TankBind could predict both the protein-ligand binding structure and their affinity. If you have any question or suggestion, please feel free to open an issue or email me at [email protected] or shuangjia zheng at [email protected].

Installation

conda create -n tankbind_py38 python=3.8
conda activate tankbind_py38

You might want to change the cudatoolkit version based on the GPU you are using.:

conda install pytorch cudatoolkit=11.3 -c pytorch

conda install torchdrug pyg biopython nglview -c milagraph -c conda-forge -c pytorch -c pyg
pip install torchmetrics tqdm mlcrate pyarrow

p2rank v2.3 could be downloaded from here:

https://github.com/rdk/p2rank/releases/download/2.3/p2rank_2.3.tar.gz

Prediction

To predict the drug-protein binding structure, check out

examples/prediction_example_using_PDB_6hd6.ipynb

Training and test dataset construction

To process the raw PDBbind data into training and test set, check out

examples/construction_PDBbind_training_and_test_dataset.ipynb

High-throughput virtual screening

TankBind also support virtual screening. In our example here, for the WDR domain of LRRK2 protein, we can screen 10,000 drug candidates in 2 minutes (or 1M in around 3 hours) with a single GPU. Check out

examples/high_throughput_virtual_screening_LRRK2_WDR.ipynb