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Enhanced Modeling via Network Theory: Adaptive Sampling of Markov State Models

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Biophysics Program and Department of Chemistry, Stanford University, Stanford, California 94305
* Corresponding author e-mail: [email protected]
†Biophysics Program.
‡Department of Chemistry.
Cite this: J. Chem. Theory Comput. 2010, 6, 3, 787–794
Publication Date (Web):February 17, 2010
https://doi.org/10.1021/ct900620b
Copyright © 2010 American Chemical Society
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    Abstract

    Computer simulations can complement experiments by providing insight into molecular kinetics with atomic resolution. Unfortunately, even the most powerful supercomputers can only simulate small systems for short time scales, leaving modeling of most biologically relevant systems and time scales intractable. In this work, however, we show that molecular simulations driven by adaptive sampling of networks called Markov State Models (MSMs) can yield tremendous time and resource savings, allowing previously intractable calculations to be performed on a routine basis on existing hardware. We also introduce a distance metric (based on the relative entropy) for comparing MSMs. We primarily employ this metric to judge the convergence of various sampling schemes but it could also be employed to assess the effects of perturbations to a system (e.g., determining how changing the temperature or making a mutation changes a system’s dynamics).

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