GaudiMM: A modular optimization platform for molecular design
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README.rst

GaudiMM

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GaudiMM, for Genetic Algorithms with Unrestricted Descriptors for Intuitive Molecular Modeling, helps to sketch new molecular designs that require complex interactions.

GaudiMM logo

Features

Full multi-objective optimization

  • Feel free to optimize H bonds, hydrophobic interactions, desolvation effects, distances between given sets of atoms, rotamers and more, without compromises.

Unprecedented customizability

  • Every gene and objective is a separate module, so they can be called on demand one or more times. This flexible approach allows very different calculations with the same mindset: exploration and evaluation.

Developer friendly

  • If the provided genes and objectives are not enough, you can always code your own ones. Check out the developer docs!

Documentation and support

Documentation source is available in docs/ subdirectory, and also compiled as HTML at this webpage.

If you need help with GaudiMM, please use the issues page of our GitHub repo. You can drop me a message at jaime.rodriguezguerra@uab.cat too.

License

GaudiMM is licensed under the Apache License, Version 2.0. Check the details in the LICENSE file.

Citation

GaudiMM is scientific software, funded by public research grants (Spanish MINECO's project CTQ2014-54071-P, Generalitat de Catalunya's project 2014SGR989 and research grant 2015FI_B00768, COST Action CM1306). If you make use of GaudiMM in scientific publications, please cite our article in JCC. It will help measure the impact of our research and future funding!

@article {JCC:JCC24847,
    author = {Rodríguez-Guerra Pedregal, Jaime and Sciortino, Giuseppe and Guasp, Jordi and Municoy, Martí and Maréchal, Jean-Didier},
    title = {GaudiMM: A modular multi-objective platform for molecular modeling},
    journal = {Journal of Computational Chemistry},
    volume = {38},
    number = {24},
    issn = {1096-987X},
    url = {http://dx.doi.org/10.1002/jcc.24847},
    doi = {10.1002/jcc.24847},
    pages = {2118--2126},
    keywords = {molecular modeling, protein-ligand docking, multi-objective optimization, genetic algorithms, metallopeptides},
    year = {2017},
}