Awesome
gnina-torch
PyTorch implementation of GNINA scoring function.
[!WARNING] GNINA version
1.3
changed the deep learning backend from Caffe to PyTorch. Therefore, PyTorch models are now nativaly supported by GNINA. Using GNINA has the advantage that the models can be used directly within the docking pipeline, instead of being used for post-processing. Thegnina-torch
project is no longer under active development.
References
@software{
gninatorch_2022,
author = {Meli, Rocco and McNutt, Andrew},
doi = {10.5281/zenodo.6943066},
month = {7},
title = {{gninatorch}},
url = {https://github.com/RMeli/gnina-torch},
version = {0.0.1},
year = {2022}
}
If you are using gnina-torch
, please consider citing the following references:
Protein-Ligand Scoring with Convolutional Neural Networks, M. Ragoza, J. Hochuli, E. Idrobo, J. Sunseri, and D. R. Koes, J. Chem. Inf. Model. 2017, 57 (4), 942-957. DOI: 10.1021/acs.jcim.6b00740
libmolgrid: Graphics Processing Unit Accelerated Molecular Gridding for Deep Learning Applications J. Sunseri and D. R. Koes, J. Chem. Inf. Model. 2020, 60 (3), 1079-1084. DOI: 10.1021/acs.jcim.9b01145
If you are using the pre-trained default2018
and dense
models from GNINA, please consider citing the following reference as well:
Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design, P. G. Francoeur, T. Masuda, J. Sunseri, A. Jia, R. B. Iovanisci, I. Snyder, and D. R. Koes, J. Chem. Inf. Model. 2020, 60 (9), 4200-4215. DOI: 10.1021/acs.jcim.0c00411
If you are using the pre-trained default
model ensemble from GNINA, please consider citing the following reference as well:
GNINA 1.0: molecular docking with deep learning, A. T. McNutt, P. Francoeur, R. Aggarwal, T. Masuda, R. Meli, M. Ragoza, J. Sunseri, D. R. Koes, J. Cheminform. 2021, 13 (43). DOI: 10.1186/s13321-021-00522-2
Installation
The gninatorch
Python package has several dependencies, including:
A full developement environment can be installed using the conda package manager and the provided conda environment file (devtools/conda-envs/gninatorch.yaml
):
conda env create -f devtools/conda-envs/gninatorch.yaml
conda activate gninatorch
Once the conda environment is created and activated, the gninatorch
package can be installed using pip as follows:
python -m pip install .
Tests
In order to check the installation, unit tests are provided and can be run with pytest:
pytest --cov=gninatorch
Usage
Training and inference modules try to follow the original Caffe implementation of gnina/scripts, however not all features are implemented.
The folder examples
includes some complete examples for training and inference.
The folder gninatorch/weights
contains pre-trained models from GNINA, converted from Caffe to PyTorch.
Pre-trained GNINA models
Pre-trained GNINA models can be loaded as follows:
from gninatorch.gnina import setup_gnina_model
model = setup_gnina_model(MODEL)
where MODEL
corresponds to the --cnn
argument in GNINA.
A single model will return log_CNNscore
and CNNaffinity
, while an ensemble of models will return log_CNNscore
, CNNaffinity
, and CNNvariance
.
Inference with pre-trained GNINA models (--cnn
argument in GNINA) is implemented in the gnina
module:
python -m gninatorch.gnina --help
Training
Training is implemented in the training
module:
python -m gninatorch.training --help
Inference
Inference is implemented in the inference
module:
python -m gninatorch.inference --help
Acknowledgments
Project based on the Computational Molecular Science Python Cookiecutter version 1.6.
The pre-trained weights of GNINA converted to PyTorch were kindly provided by Andrew McNutt (@drewnutt).
Copyright (c) 2021-2022, Rocco Meli