A Multi-Objective Optimization Energy Approach to Predict the Ligand Conformation in a Docking Process

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{sandoval:2013:EuroGP,
  author =       "Angelica Sandoval-Perez and David Becerra and 
                 Diana Vanegas and Daniel Restrepo-Montoya and Fernando Nino",
  title =        "A Multi-Objective Optimization Energy Approach to
                 Predict the Ligand Conformation in a Docking Process",
  booktitle =    "Proceedings of the 16th European Conference on Genetic
                 Programming, EuroGP 2013",
  year =         "2013",
  month =        "3-5 " # apr,
  editor =       "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and 
                 A. Sima Uyar and Bin Hu",
  series =       "LNCS",
  volume =       "7831",
  publisher =    "Springer Verlag",
  address =      "Vienna, Austria",
  pages =        "181--192",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-37206-3",
  DOI =          "doi:10.1007/978-3-642-37207-0_16",
  abstract =     "This work proposes a multi-objective algorithmic
                 method for modelling the prediction of the conformation
                 and configuration of ligands in receptor-ligand
                 complexes by considering energy contributions of
                 molecular interactions. The proposed approach is an
                 improvement over others in the field, where the
                 principle insight is that a Pareto front helps to
                 understand the tradeoffs in the actual problem. The
                 method is based on three main features: (i)
                 Representation of molecular data using a trigonometric
                 model; (ii) Modelling of molecular interactions with
                 all-atoms force field energy functions and (iii)
                 Exploration of the conformational space through a
                 multi-objective evolutionary algorithm. The performance
                 of the proposed model was evaluated and validated over
                 a set of well known complexes. The method showed a
                 promising performance when predicting ligands with high
                 number of rotatable bonds.",
  notes =        "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in
                 conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013
                 and EvoApplications2013",
}

Genetic Programming entries for Angelica Sandoval-Perez David Becerra Diana Vanegas Daniel Restrepo-Montoya Luis Fernando Nino Vasquez

Citations