Metadata-Version: 1.1
Name: AdaptivePELE
Version: 1.7.1
Summary: Enhanced sampling of molecular simulations
Home-page: https://github.com/AdaptivePELE/AdaptivePELE
Author: Daniel Lecina, Joan Francesc Gilabert
Author-email: danilecina@gmail.com, cescgina@gmail.com
License: UNKNOWN
Project-URL: Documentation, https://adaptivepele.github.io/AdaptivePELE//
Project-URL: Source, https://github.com/AdaptivePELE/AdaptivePELE
Project-URL: Tracker, https://github.com/AdaptivePELE/AdaptivePELE/issues
Description: ============
        AdaptivePELE
        ============
        
        
        |MIT license| |GitHub release| |PyPI release| |Conda release| |DOI|
        
        AdaptivePELE is a Python module to perform enhancing sampling of molecular
        simulation built around the Protein Energy Landscape Exploration method (`PELE <https://pele.bsc.es/pele.wt>`_) developed in the Electronic and Atomic Protein Modelling grop (`EAPM <https://www.bsc.es/discover-bsc/organisation/scientific-structure/electronic-and-atomic-protein-modeling-eapm>`_) at the Barcelona Supercomputing Center (`BSC <https://www.bsc.es>`_).
        
        Usage
        -----
        
        AdaptivePELE is called with a control file as input
        parameter. The control file is a json document that contains 4 sections:
        general parameters, simulation parameters, clustering parameters and spawning
        parameters. The first block refers to general parameters of the adaptive run,
        while the other three blocks configure the three steps of an adaptive sampling
        run, first run a propagation algorithm (simulation), then cluster the
        trajectories obtained (clustering) and finally select the best point to start
        the next iteration (spawning).
        
        An example of usage::
        
            python -m AdaptivePELE.adaptiveSampling controlFile.conf
        
        Installation
        ------------
        
        There are two methods to install AdaptivePELE, from repositories, either PyPI or Conda (recommended), or directly from source.
        
        To install from PyPI simply run::
        
            pip install AdaptivePELE
        
        To install from Conda simply run::
        
            conda install -c nostrumbiodiscovery -c conda-forge adaptive_pele 
        
        To install from source, you need to install and compile cython files in the base folder with::
        
            git clone https://github.com/AdaptivePELE/AdaptivePELE.git
            cd AdaptivePELE
            python setup.py build_ext --inplace
        
        Also, if AdaptivePELE was not installed in a typical library directory, a common option is to add it to your local PYTHONPATH::
        
            export PYTHONPATH="/location/of/AdaptivePELE:$PYTHONPATH"
        
        Documentation
        -------------
        
        The documentation for AdaptivePELE can be found `here <https://adaptivepele.github.io/AdaptivePELE/>`_
        
        
        Contributors
        ------------
        `Daniel Lecina <https://github.com/lecina>`_, `Joan Francesc Gilabert <https://github.com/cescgina>`_, `Oriol Gracia <https://github.com/OriolGraCar>`_, `Daniel Soler <https://github.com/danielSoler93>`_
        
        Mantainer
        ---------
        Joan Francesc Gilabert (cescgina@gmail.com)
        
        Citation 
        --------
        
        AdaptivePELE is research software. If you make use of AdaptivePELE in scientific publications, please cite it. The BibTeX reference is::
        
            @article{Lecina2017,
            author = {Lecina, Daniel and Gilabert, Joan Francesc and Guallar, Victor},
            doi = {10.1038/s41598-017-08445-5},
            issn = {2045-2322},
            journal = {Scientific Reports},
            number = {1},
            pages = {8466},
            pmid = {28814780},
            title = {{Adaptive simulations, towards interactive protein-ligand modeling}},
            url = {http://www.nature.com/articles/s41598-017-08445-5},
            volume = {7},
            year = {2017}
            }
        
        
        .. |MIT license| image:: https://img.shields.io/badge/License-MIT-blue.svg
           :target: https://lbesson.mit-license.org/
        
        
        .. |GitHub release| image:: https://img.shields.io/github/release/AdaptivePELE/AdaptivePELE.svg
            :target: https://github.com/AdaptivePELE/AdaptivePELE/releases/
        
        .. |PyPI release| image:: https://img.shields.io/pypi/v/AdaptivePELE.svg
            :target: https://pypi.org/project/AdaptivePELE/
        
        .. |DOI| image:: https://zenodo.org/badge/DOI/10.1038/s41598-017-08445-5.svg
          :target: https://doi.org/10.1038/s41598-017-08445-5
          
        .. |Conda release| image:: https://anaconda.org/nostrumbiodiscovery/adaptive_pele/badges/version.svg
          :target: https://anaconda.org/NostrumBioDiscovery/adaptive_pele
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 2.7
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX :: Linux
Classifier: Intended Audience :: Science/Research
