On the advantages of exploiting memory in Markov state models for biomolecular dynamics-Reference-Cited by-同舟云学术

On the advantages of exploiting memory in Markov state models for biomolecular dynamics

Published:2020-07-07 Issue:1 Volume:153 Page:014105
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:J. Chem. Phys.

Author:

Cao Siqin¹^ORCID,Montoya-Castillo Andrés²^ORCID,Wang Wei¹,Markland Thomas E.²^ORCID,Huang Xuhui¹³⁴^ORCID

Affiliation:

1. Department of Chemistry, The Hong Kong University of Science and Technology, Kowloon, Hong Kong

2. Department of Chemistry, Stanford University, Stanford, California 94305, USA

3. State Key Laboratory of Molecular Neuroscience, Center of System Biology and Human Health, The Hong Kong University of Science and Technology, Kowloon, Hong Kong

4. The Hong Kong University of Science and Technology-Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen, China

Funder

U.S. Department of Energy

Research Grants Council, University Grants Committee

Innovation and Technology Commission

Science, Technology and Innovation Commission of Shenzhen Municipality

Camille and Henry Dreyfus Foundation

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://aip.scitation.org/doi/pdf/10.1063/5.0010787

Reference95 articles.

1. G. R. Bowman, V. S. Pande, and F. Noe, An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation (Springer Netherlands, 2014), p. 139.

2. Markov models of molecular kinetics: Generation and validation

3. Automatic discovery of metastable states for the construction of Markov models of macromolecular conformational dynamics

4. Building Markov state models along pathways to determine free energies and rates of transitions

5. Simulating Replica Exchange: Markov State Models, Proposal Schemes, and the Infinite Swapping Limit

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