ethmodlab/bcpaff

Exploring protein-ligand binding affinity prediction with electron density-based geometric deep learning

bcpaff - Exploring protein-ligand binding affinity prediction with electron density-based geometric deep learning

Details are described in our [paper] (TODO: add link). Please cite if you use this work.

To setup the conda environment, install Multiwfn, and download the datasets, just run the following in your $CWD:

cd bcpaff
make

(this step uses mamba, you can change it to conda by using make with_conda instead).

Structure preparation and training (remove --test_run to run on all structures; remove --cluster_options no_cluster to run via Slurm):

make data_processing

(basically running bcpaff.data_processing.data_processing)

ML model training:

make ml_experiments

To interactively visualize BCPs in Jupyter Notebook:

from bcpaff.qtaim.qtaim_viewer import QtaimViewer 
from bcpaff.qtaim.qtaim_reader import QtaimProps 

qp = QtaimProps(basepath="PATH_TO_COMPOUND_FOLDER")
v = QtaimViewer(qp)
v.show()