zrt/pocketanchor

Learning Structure-based Subpocket Representations for Protein-Ligand Interaction Prediction

PocketAnchor

Learning Structure-based Subpocket Representations for Protein-Ligand Interaction Prediction

Requirements

cuda                11.2
python              3.7.4
torch               1.7.1
torch-geometric     1.6.3
numpy               1.19.0
pandas              1.2.4
rdkit               2020.03.3.0
scikit-learn        0.21.3 
scipy               1.6.3 
tensorboard         2.4.1

Reproducing results

1. Prepare a environment that satisfying the above requirements;

2. Download the trained model files:

   • PocketAnchor-models.zip (105 MB)

3. Download the input data files:

   • PocketAnchor-data-AffinityInteractionDistance.zip (5.68GB)

   • PocketAnchor-data-PocketDetection.zip (2.82GB)

4. Run the inference scripts below;

5. The results can be found in [TASK]/results/[FOLDER]/.

1. PocketAnchor-site

Protein ligand binding site prediction

python runPrediction.py --task PocketDetection --dataset COACH420
python runPrediction.py --task PocketDetection --dataset HOLO4k

2. PocketAnchor-affinity

Protein-ligand binding affinity prediction

python runPrediction.py --task Affinity --setting original
python runPrediction.py --task Affinity --setting newprotein
python runPrediction.py --task Affinity --setting expanded

3. PocketAnchor-interaction

Non-covalent interaction prediction

python runPrediction.py --task Interaction

4. PocketAnchor-distance

Protein ligand binding pose classfication

python runPrediction.py --task Distance