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.
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, PCN_main_train.py To test/evaluate, run 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 --mlhdf-fn pdbbind2019_core_docking.hdf --csv-fn vina_delta.csv --model-path /Model_Checkpoint/PCN_a.pth
PECAN was created by Heesung Shim (shim2@llnl.gov)
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.
See LICENSE for more details.
SPDX-License-Identifier: MIT
LLNL-CODE-858561