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Enhanced sampling of molecular simulations

Project description

MIT license GitHub release PyPI release DOI

AdaptivePELE is a Python module to perform enhancing sampling of molecular simulation built around the Protein Energy Landscape Exploration method (PELE) developed in the Electronic and Atomic Protein Modelling grop (EAPM) at the Barcelona Supercomputing Center (BSC).


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


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 adaptive_pele

To install from source, you need to install and compile cython files in the base folder with:

git clone
cd AdaptivePELE
python 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"


The documentation for AdaptivePELE can be found here


Joan Francesc Gilabert (


AdaptivePELE is research software. If you make use of AdaptivePELE in scientific publications, please cite it. The BibTeX reference is:

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 = {},
volume = {7},
year = {2017}

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