A Direct Approach to Conformational Dynamics Based on Hybrid Monte Carlo

By Christof Schutte, Alexander Fischer, Wilhelm Huisinga, Peter Deuflhard. 1999

Subfields: Computational Chemistry & Drug Discovery, AI for Physical Sciences

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Citations in the corpus

Multiensemble Markov models of molecular thermodynamics and kinetics (2016, unknown): “[22]”
Diffusion Maps, Reduction Coordinates, and Low Dimensional Representation of Stochastic Systems (2008, methodology): “Our computational approach is closely related to the transfer operator approach [46], which also computes an approximation to the eigenfunctions of the FP operator [27], and to Perron cluster analysis [18, 19].”
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.”
Shadow hybrid Monte Carlo: an efficient propagator in phase space of macromolecules (2004, background): “Conformational dynamics [53, 54] is an application that might benefit from SHMC.”
Variational Approach for Learning Markov Processes from Time Series Data (2017, methodology): “There are other formalisms to describe Markovian dynamics, for example, the Markov propagator or the weighted Markov propagator, also called transfer operator [52].”
Identification of slow molecular order parameters for Markov model construction. (2013, methodology): “Equivalent expressions are obtained by expressing the eigenfunctions in different weighted spaced, leading to the transfer operator formulation [81], or the symmetrized propagator formulation [14].”
VAMPnets for deep learning of molecular kinetics (2017, methodology): “The Markov modeling approach [7–12] has been a driving force in the development of kinetic modeling techniques from MD mass data, chieﬂy as it facilitates a divide-and-conquer approach to integrate short, distributed MD simulations into a model of the long-timescale behavior.”
A Variational Approach to Modeling Slow Processes in Stochastic Dynamical Systems (2012, methodology): “In macromolecules, such slowly-exchanging sets are called conformations, hence the union of the slowest dynamical processes are there termed conformation dynamics [35, 37].”
Markov models of molecular kinetics: generation and validation. (2011, methodology): “In principle, the full dynamical information of the discretized system can be obtained by initiating trajectories of length τ out of each state i as long as we draw the starting points of these simulations from a local equilibrium density μi(x) [42, 43, 67].”
An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation (2014, methodology): “When the particular kind of dynamics is known it can be written in a more specific form [37].”
Markov state models of biomolecular conformational dynamics (2014, background): “The foundational work on coarse-graining MSMs was the development of PCCA [31] and PCCA+ [32].”