Markov models of molecular kinetics: generation and validation.

By Jan-Hendrik Prinz, Hao Wu, Marco Sarich, Bettina G. Keller, Martin Senne, Martin Held, John D. Chodera, Christof Schutte, Frank No'e. 2011

1,235 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Molecular Biology, Spectroscopy

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Citations in the corpus

Deep Generative Markov State Models (2018, methodology): “01 in the Prinz potential V (xt) introduced in [22] (Fig.”
Variational Approach for Learning Markov Processes from Time Series Data (2017, methodology): “zedsolutionmethods,suchastheLASSOmethod[59].Alternatively,feature functionsf andg thatallowEq.(1)tohaveaprobabilisticinterpretation(e.g. inMSMs),K canbeestimatedbyamaximum-likelihoodorBayesianmethods [48,39]. However, as yet, it is still unclear what are the optimal choices for f and g -eithergivena”
Error bounds for kernel-based approximations of the Koopman operator (2023, methodology): “Koopman methods have found widespread application in system identification [5], control [29, 49, 30, 22, 56], sensor placement [38], molecular dynamics [57, 51, 42, 43, 23, 64], and many other fields.”
Data-Driven Model Reduction and Transfer Operator Approximation (2017, background): “Approximate convergence of implied timescales with increasing lag time has become a standard model validation criterion in molecular dynamics [44].”
Protein storytelling through physics (2020, background): “One strategy for computational parallelization is Markov state modeling ( 62 – 68 ).”
Scalable emulation of protein equilibrium ensembles with generative deep learning (2025, positive): “To mitigate the sampling problem, MD data was reweighed towards equilibrium using either Markov State Models [20], or weights from experimental data (see S.3.5.3), when possible.”
Data-driven non-Markovian closure models (2014, methodology): “Although the present article is motivated primarily by the need for LOMs in climate modeling, similar issues arise in many other areas of the physical and life sciences, and LOMs are becoming a key tool in various disciplines as diverse as astrophysics [2], biological neuronal modeling [3], molecula”
Fuzzy spectral clustering by PCCA+: application to Markov state models and data classification (2013, background): “Today it is often referred to as Markov state modelling (MSM), see Chodera et al. (2007), Bowman et al. (2009), Sarich et al. (2010), Prinz et al. (2011).”
Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations (2011, background): “ab initio protein folding for a millisecond folder NTL9(1-39).”