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forked from RMeli/aescore

Learning Protein-Ligand Properties with Atomic Environment Vectors

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bigginlab/aescore

AEScore: Protein-Ligand Binding Affinity with Atomic Environment Vectors

flake8 mypy pytest codecov

Learning protein-ligand binding affinity using atomic environment vectors.

Installation

conda create -f devtools/conda-envs/ael-test.yaml
pip install .

Usage

Training

python -m ael.train --help

Inference

python -m ael.predict --help

Test

Run tests:

pytest

References

  • Justin S. Smith, Olexandr Isayev, Adrian E. Roitberg. ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost. Chemical Science,(2017), DOI: 10.1039/C6SC05720A
  • Gao, Xiang; Ramezanghorbani, Farhad; Isayev, Olexandr; Smith, Justin; Roitberg, Adrian (2020): TorchANI: A Free and Open Source PyTorch Based Deep Learning Implementation of the ANI Neural Network Potentials. ChemRxiv. Preprint. DOI: 10.26434/chemrxiv.12218294.v1
  • R. J. Gowers, M. Linke, J. Barnoud, T. J. E. Reddy, M. N. Melo, S. L. Seyler, D. L. Dotson, J. Domanski, S. Buchoux, I. M. Kenney, and O. Beckstein. MDAnalysis: A Python package for the rapid analysis of molecular dynamics simulations. In S. Benthall and S. Rostrup, editors, Proceedings of the 15th Python in Science Conference, pages 98-105, Austin, TX, 2016. SciPy, DOI: 10.25080/majora-629e541a-00e.
  • N. Michaud-Agrawal, E. J. Denning, T. B. Woolf, and O. Beckstein. MDAnalysis: A Toolkit for the Analysis of Molecular Dynamics Simulations. J. Comput. Chem. 32 (2011), 2319-2327, DOI: 10.1002/jcc.21787
  • O'Boyle, N.M., Banck, M., James, C.A. et al. Open Babel: An open chemical toolbox. J Cheminform 3, 33 (2011). DOI: 10.1186/1758-2946-3-33
  • N.M. O’Boyle, C. Morley and G.R. Hutchison. Pybel: a Python wrapper for the OpenBabel cheminformatics toolkit. Chem. Cent. J. 2008, 2, 5. DOI: 10.1186/1752-153X-2-5

Copyright

Copyright (c) 2020-2021, Rocco Meli

Acknowledgements

Project based on the Computational Molecular Science Python Cookiecutter version 1.3.

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Learning Protein-Ligand Properties with Atomic Environment Vectors

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