README.md

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. The gnina-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:

• PyTorch
• PyTorch-Ignite
• libmolgrid

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).

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Copyright (c) 2021-2022, Rocco Meli