Markov state models: From an art to a science
Markov state models (MSMs) are a powerful framework for analyzing dynamical systems,
such as molecular dynamics (MD) simulations, that have gained widespread use over the …
such as molecular dynamics (MD) simulations, that have gained widespread use over the …
RNA structural dynamics as captured by molecular simulations: a comprehensive overview
With both catalytic and genetic functions, ribonucleic acid (RNA) is perhaps the most
pluripotent chemical species in molecular biology, and its functions are intimately linked to …
pluripotent chemical species in molecular biology, and its functions are intimately linked to …
[HTML][HTML] VAMPnets for deep learning of molecular kinetics
There is an increasing demand for computing the relevant structures, equilibria, and long-
timescale kinetics of biomolecular processes, such as protein-drug binding, from high …
timescale kinetics of biomolecular processes, such as protein-drug binding, from high …
Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems
Machine learning encompasses tools and algorithms that are now becoming popular in
almost all scientific and technological fields. This is true for molecular dynamics as well …
almost all scientific and technological fields. This is true for molecular dynamics as well …
Machine learning and data science in soft materials engineering
AL Ferguson - Journal of Physics: Condensed Matter, 2017 - iopscience.iop.org
In many branches of materials science it is now routine to generate data sets of such large
size and dimensionality that conventional methods of analysis fail. Paradigms and tools from …
size and dimensionality that conventional methods of analysis fail. Paradigms and tools from …
Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation
Classical molecular dynamics simulates the time evolution of molecular systems through the
phase space spanned by the positions and velocities of the constituent atoms. Molecular …
phase space spanned by the positions and velocities of the constituent atoms. Molecular …
An efficient path classification algorithm based on variational autoencoder to identify metastable path channels for complex conformational changes
Conformational changes (ie, dynamic transitions between pairs of conformational states)
play important roles in many chemical and biological processes. Constructing the Markov …
play important roles in many chemical and biological processes. Constructing the Markov …
[HTML][HTML] Nonlinear discovery of slow molecular modes using state-free reversible VAMPnets
The success of enhanced sampling molecular simulations that accelerate along collective
variables (CVs) is predicated on the availability of variables coincident with the slow …
variables (CVs) is predicated on the availability of variables coincident with the slow …
Collective variables for the study of long-time kinetics from molecular trajectories: theory and methods
F Noé, C Clementi - Current opinion in structural biology, 2017 - Elsevier
Collective variables are an important concept to study high-dimensional dynamical systems,
such as molecular dynamics of macromolecules, liquids, or polymers, in particular to define …
such as molecular dynamics of macromolecules, liquids, or polymers, in particular to define …
tICA-metadynamics: accelerating metadynamics by using kinetically selected collective variables
M M. Sultan, VS Pande - Journal of chemical theory and …, 2017 - ACS Publications
Metadynamics is a powerful enhanced molecular dynamics sampling method that
accelerates simulations by adding history-dependent multidimensional Gaussians along …
accelerates simulations by adding history-dependent multidimensional Gaussians along …