English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/96864
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

Title

Randomized tree construction algorithm to explore energy landscapes

AuthorsJaillet, Léonard ; Corcho, Francesc J.; Pérez, Juan Jesús; Cortés, Juan
KeywordsRobot path planning algorithms
Monte Carlo methods
Conformational transition paths
Peptides
Energy landscape exploration
Issue Date2011
PublisherJohn Wiley & Sons
CitationJournal of Computational Chemistry 32(16): 3464-3474 (2011)
AbstractIn 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.
Publisher version (URL)http://dx.doi.org/10.1002/jcc.21931
URIhttp://hdl.handle.net/10261/96864
DOI10.1002/jcc.21931
Identifiersdoi: 10.1002/jcc.21931
issn: 0192-8651
e-issn: 1096-987X
Appears in Collections:(IRII) Artículos
Files in This Item:
File Description SizeFormat 
Randomized tree.pdf897,51 kBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 

Related articles:


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.