PyTorch implementation of GNINA scoring function.

The gnina 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 create -f devtools/conda-envs/gninatorch.yaml
conda activate gninatorch

Once the conda environment is created and activated, the gnina package can be installed using pip as follows:

python -m pip install .

In order to check the installation, unit tests are provided and can be run with pytest:

pytest --cov=gnina

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.

Training is implemented in the training module:

python -m gnina.training --help

Inference is implemented in the inference module:

python -m gnina.inference --help

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

Copyright (c) 2021, Rocco Meli

Project based on the Computational Molecular Science Python Cookiecutter version 1.6.