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http://hdl.handle.net/10261/96864
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Título: | Randomized tree construction algorithm to explore energy landscapes |
Autor: | Jaillet, Léonard ![]() |
Palabras clave: | Robot path planning algorithms Monte Carlo methods Conformational transition paths Peptides Energy landscape exploration |
Fecha de publicación: | 2011 |
Editor: | John Wiley & Sons |
Citación: | Journal of Computational Chemistry 32(16): 3464-3474 (2011) |
Resumen: | In this work, a new method for exploring conformational energy landscapes is described. The method, called transition-rapidly exploring random tree (T-RRT), combines ideas from statistical physics and robot path planning algorithms. A search tree is constructed on the conformational space starting from a given state. The tree expansion is driven by a double strategy: on the one hand, it is naturally biased toward yet unexplored regions of the space; on the other, a Monte Carlo-like transition test guides the expansion toward energetically favorable regions. The balance between these two strategies is automatically achieved due to a self-tuning mechanism. The method is able to efficiently find both energy minima and transition paths between them. As a proof of concept, the method is applied to two academic benchmarks and the alanine dipeptide. Copyright © 2011 Wiley Periodicals, Inc. |
Versión del editor: | http://dx.doi.org/10.1002/jcc.21931 |
URI: | http://hdl.handle.net/10261/96864 |
DOI: | 10.1002/jcc.21931 |
Identificadores: | doi: 10.1002/jcc.21931 issn: 0192-8651 e-issn: 1096-987X |
Aparece en las colecciones: | (IRII) Artículos |
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Randomized tree.pdf | 897,51 kB | Adobe PDF | ![]() Visualizar/Abrir |
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