Identifying correct binding poses of ligands is important in docking based virtual high-throughput screening. This code implements two convolutional neural network approaches: a 3D convolutional neural network (3D-CNN) and a point cloud network (PCN) to improves virtual high-throughput screening to identify novel molecules against each target protein. The code is written in python with Pytorch.
Both 3D-CNN and PCN use a 3D atomic representation as input data in a Hierarchical Data Format (HDF5). See (https://github.com/LLNL/FAST/) for more information about this HDF5 format.
To train, 3D-CNN/CNN_train.py To test/evaluate, run 3D-CNN/CNN_eval.py Here is an example comand to evaluate a pre-trained 3D-CNN model:
python CNN_eval.py --data-hdf pdbbind2019_core_docking.hdf --data-csv pdbbind2019_core.csv --data-chdf pdbbind2019_core_crystal.hdf --model-path /Model_Checkpoint/CNN_a.pth --model-type 6
To train, 3D-CNN/PCN_main_train.py To test/evaluate, run 3D-CNN/PCN_main_eval.py. Here is an example comand to evaluate a pre-trained PCN model:
python PCN_main_eval.py --device-name cuda:1 --data-dir /Data --dataset-type 1 --mlhdf-fn pdbbind2019_core_docking.hdf --csv-fn pdbbind2019_core.csv --cmlhdf-fn pdbbind2019_core_crystal.hdf --model-path /Model_Checkpoint/PCN_a.pth
We trained all of the networks above on pdbbind 2019 datasets. Particularly, we used the refined set for training, and evaluated the models on the core set. Data and checkpoint files can be found in (https://drive.google.com/drive/folders/17aTy-5Epvvwcpn_Kxwn7ksyUU2EGed3g?usp=sharing).
For other dataset, please contact the author.
PECAN was created by Heesung Shim (hsshim@ucdavis.edu)
PECAN is distributed under the terms of the MIT license. All new contributions must be made under this license. See LICENSE in this directory for the terms of the license.