2003
DOI: 10.1089/10665270360688011
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Stochastic Roadmap Simulation: An Efficient Representation and Algorithm for Analyzing Molecular Motion

Abstract: Classic molecular motion simulation techniques, such as Monte Carlo (MC) simulation, generate motion pathways one at a time and spend most of their time in the local minima of the energy landscape defined over a molecular conformation space. Their high computational cost prevents them from being used to compute ensemble properties (properties requiring the analysis of many pathways). This paper introduces stochastic roadmap simulation (SRS) as a new computational approach for exploring the kinetics of molecula… Show more

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Cited by 94 publications
(121 citation statements)
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“…Earlier work on SRS compared it with Monte Carlo simulation and showed that SRS is faster by several orders of magnitude [3]. The comparison with experimental data serves as a test of the methodology, and the success further validates the SRS method and indicates its potential as a general tool for studying protein folding kinetics.…”
Section: Introductionmentioning
confidence: 86%
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“…Earlier work on SRS compared it with Monte Carlo simulation and showed that SRS is faster by several orders of magnitude [3]. The comparison with experimental data serves as a test of the methodology, and the success further validates the SRS method and indicates its potential as a general tool for studying protein folding kinetics.…”
Section: Introductionmentioning
confidence: 86%
“…The main idea of PRM methods is to sample at random the space of all robot configurationsa space conceptually similar to a protein conformation space-and construct a graph that captures the connectivity of this space. Methods derived from PRM have been applied to ligand-protein docking [17], protein folding [3,2], and RNA folding [19]. In our earlier work, we used SRS to study protein folding, but the results were compared only with those obtained from Monte Carlo simulation.…”
Section: Related Workmentioning
confidence: 99%
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“…Our long term goal is to study high-dimensional problems [12] such as those arising in planning with flexible objects [13], reconfigurable robots [17], complex planning instances [16], and computational biology search problems [1,3]. Such problems test the limits of current planner implementations.…”
Section: Introductionmentioning
confidence: 99%