Skip to content
/ bcpaff Public

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

License

Notifications You must be signed in to change notification settings

cisert/bcpaff

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

2 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

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

Details are described in our paper. 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()

About

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

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published