NDLI: A memetic hybrid method for the Molecular Distance Geometry Problem with incomplete information

Content Provider	IEEE Xplore Digital Library
Author	Nobile, M.S. Citrolo, A.G. Cazzaniga, P. Besozzi, D. Mauri, G.
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci., Univ. of Milano, Milan, Italy (Besozzi, D.) \|\| Dept. of Comput. Sci., Univ. of Milano-Bicocca, Milan, Italy (Nobile, M.S.; Citrolo, A.G.; Mauri, G.) \|\| Dept. of Human & Social Sci., Univ. of Bergamo, Bergamo, Italy (Cazzaniga, P.)
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.
Starting Page	1014
Ending Page	1021
File Size	3143110
Page Count	8
File Format	PDF
e-ISBN	9781479914883
DOI	10.1109/CEC.2014.6900386
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Atomic measurements Proteins Vectors Nuclear magnetic resonance Sociology Statistics Three-dimensional displays
Content Type	Text
Resource Type	Article

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