A memetic hybrid method for the Molecular Distance Geometry Problem with incomplete information (Contributo in atti di convegno)

Type
Label
  • A memetic hybrid method for the Molecular Distance Geometry Problem with incomplete information (Contributo in atti di convegno) (literal)
Anno
  • 2014-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1109/CEC.2014.6900386 (literal)
Alternative label
  • Nobile M.S.; Citrolo A.G.; Cazzaniga P.; Besozzi D.; Mauri G. (2014)
    A memetic hybrid method for the Molecular Distance Geometry Problem with incomplete information
    in 2014 IEEE Congress on Evolutionary Computation (CEC 2014)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Nobile M.S.; Citrolo A.G.; Cazzaniga P.; Besozzi D.; Mauri G. (literal)
Pagina inizio
  • 1014 (literal)
Pagina fine
  • 1021 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.scopus.com/inward/record.url?eid=2-s2.0-84908577886&partnerID=q2rCbXpz (literal)
Note
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Department of Computer Science, Systems and Communication, University of Milano-Bicocca, Milano, Italy; Department of Human and Social Sciences, University of Bergamo, Bergamo, Italy; Department of Computer Science, University of Milano, Milano, Italy; Institute for Systems Analysis and Computer Science Antonio Ruberti, CNR, Rome, Italy (literal)
Titolo
  • A memetic hybrid method for the Molecular Distance Geometry Problem with incomplete information (literal)
Abstract
  • The definition of computational methodologies for the inference of molecular structural information plays a relevant role in disciplines as drug discovery and metabolic engineering, since the functionality of a biochemical molecule is determined by its three-dimensional structure. In this work, we present an automatic methodology to solve the Molecular Distance Geometry Problem, that is, to determine the best three-dimensional shape that satisfies a given set of target inter-atomic distances. In particular, our method is designed to cope with incomplete distance information derived from Nuclear Magnetic Resonance measurements. To tackle this problem, that is known to be NP-hard, we present a memetic method that combines two soft-computing algorithms - Particle Swarm Optimization and Genetic Algorithms - with a local search approach, to improve the effectiveness of the crossover mechanism. We show the validity of our method on a set of reference molecules with a length ranging from 402 to 1003 atoms. (literal)
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