LUNA

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.. image:: https://github.com/keiserlab/LUNA/blob/master/docs/source/_static/luna.svg

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LUNA [1]_ is an object-oriented Python 3 toolkit for drug design that makes it easy to analyze very large data sets of 3D molecular structures and complexes, and that allows identifying, filtering, and visualizing atomic interactions.

LUNA also implements three hashed interaction fingerprints (IFP): Extended Interaction FingerPrint (EIFP), Functional Interaction FingerPrint (FIFP), and Hybrid Interaction FingerPrint (HIFP) -- inspired by ECFP [2], FCFP [2], and E3FP [3]. These IFPs encode molecular interactions at different levels of detail, provide several functionalities to trace individual bits back to their original atomic substructures in the context of the binding site, and are RDKit-compatible.

Documentation is hosted by ReadTheDocs_, and development occurs on GitHub_.

Installation and Usage

The latest stable release (and required dependencies) can be installed as follows:

1. Download LUNA’s environment.yml <https://github.com/keiserlab/LUNA/blob/master/luna-env.yml>_ file.

2. Create the Conda environment using the downloaded file:

   conda env create -f <LUNA-ENV-FILE>

3. After creating the Conda environment, activate it:

   conda activate luna-env

4. Finally, install LUNA from Pip:

   pip install luna

For additional installation options and usage instructions, refer to the documentation <http://luna-toolkit.readthedocs.io>_.

License

LUNA is available under the |license|.

References

.. [1] |afassio2022| .. [2] |rogers2010| .. [3] |axen2017|

.. substitutions

.. |license| replace:: MIT License_ .. _MIT License: https://github.com/keiserlab/LUNA/blob/master/LICENSE

.. _RDKit: http://www.rdkit.org .. _GitHub: https://github.com/keiserlab/LUNA .. _paper repository: https://github.com/keiserlab/luna-paper .. _ReadTheDocs: http://luna-toolkit.readthedocs.io

.. |afassio2022_bioRxiv| image:: https://img.shields.io/badge/bioRxiv-136705-blue.svg :target: https://doi.org/10.1101/2022.05.25.493419 :alt: Access the preprint on bioRxiv .. |afassio2022_doi| image:: https://img.shields.io/badge/doi-10.1021/acs.jmedchem.7b00696-blue.svg :target: https://doi.org/10.1021/acs.jcim.2c00695 :alt: Access the paper .. |afassio2022| replace:: Fassio, A. V.; Shub, L.; Ponzoni, L.; McKinley, J.; O’Meara, M. J.; Ferreira, R. S.; Keiser, M. J.; de Melo Minardi, R. C. Prioritizing Virtual Screening with Interpretable Interaction Fingerprints. J. Chem. Inf. Model. 2022. |afassio2022_doi| |afassio2022_bioRxiv|

.. |axen2017_doi| image:: https://img.shields.io/badge/doi-10.1021/acs.jmedchem.7b00696-blue.svg :target: http://dx.doi.org/10.1021/acs.jmedchem.7b00696 :alt: Access the paper .. |axen2017| replace:: Axen, S. D.; Huang, X.-P.; Cáceres, E. L.; Gendelev, L.; Roth, B. L.; Keiser, M. J. A Simple Representation of Three-Dimensional Molecular Structure. J. Med. Chem. 2017, 60 (17), 7393–7409. |axen2017_doi| |bioRxiv| |F1000 recommended|

.. |rogers2010_doi| image:: https://img.shields.io/badge/doi-10.1021/ci100050t-blue.svg :target: http://dx.doi.org/10.1021/ci100050t :alt: Access the paper .. |rogers2010| replace:: Rogers, D.; Hahn, M. Extended-Connectivity Fingerprints. J. Chem. Inf. Model. 2010, 50 (5), 742–754. |rogers2010_doi|

.. |Build Status| image:: https://travis-ci.org/keiserlab/luna.svg?branch=master :target: https://travis-ci.org/keiserlab/luna :alt: Build Status .. |Docs Status| image:: http://readthedocs.org/projects/luna/badge/?version=latest :target: http://luna-toolkit.readthedocs.io/en/latest/?badge=latest :alt: Documentation Status .. |Coveralls Status| image:: https://coveralls.io/repos/github/keiserlab/luna/badge.svg?branch=master :target: https://coveralls.io/github/keiserlab/luna?branch=master :alt: Code Coverage .. |PyPi Version| image:: https://img.shields.io/pypi/v/luna.svg :target: https://pypi.python.org/pypi/luna :alt: Package on PyPi .. |License| image:: https://img.shields.io/badge/License-MIT-yellow.svg :target: https://github.com/keiserlab/LUNA/blob/master/LICENSE .. |F1000 recommended| image:: http://cdn.f1000.com.s3.amazonaws.com/images/badges/badgef1000.gif :target: http://f1000.com/prime/727824514?bd=1 :alt: Access the recommendation on F1000Prime :width: 120px :scale: 75 % .. |bioRxiv| image:: https://img.shields.io/badge/bioRxiv-136705-blue.svg :target: https://doi.org/10.1101/136705 :alt: Access the preprint on bioRxiv