Source code for the NeurIPS 2022 paper TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction

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=0.1.2 pyg biopython nglview jupyterlab -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

Test set evaluation

We include the script for reproducing the self-dock result in

examples/testset_evaluation_cleaned.ipynb

The test_dataset is constructed using the notebook in "Dataset construction" section.

Prediction

We use the prediction of the structure of protein ABL1 in complex with two drugs, Imatinib and compound6 (PDB: 6HD6) as an example for predicting the drug-protein binding structure.

examples/prediction_example_using_PDB_6hd6.ipynb

Dataset construction

Scripts for training/test dataset construction is provided in:

examples/construction_PDBbind_training_and_test_dataset.ipynb.ipynb

The Script I used to train the model is

python main.py -d 0 -m 0 --batch_size 5 --label baseline --addNoise 5 --use_equivalent_native_y_mask

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

Reference

Coming soon...