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.
Nobile, M., Citrolo, A., Cazzaniga, P., Besozzi, D., Mauri, G. (2014). A memetic hybrid method for the Molecular Distance Geometry Problem with incomplete information. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014 (pp.1014-1021). Institute of Electrical and Electronics Engineers Inc. [10.1109/CEC.2014.6900386].
A memetic hybrid method for the Molecular Distance Geometry Problem with incomplete information
NOBILE, MARCO SALVATORE;CITROLO, ANDREA GAETANO;BESOZZI, DANIELA;MAURI, GIANCARLO
2014
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.
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