Unsupervised learning is becoming an essential tool to analyze the increasingly large
amounts of data produced by atomistic and molecular simulations, in material science, solid …

Highlights•Advances in machine learning are changing the study of protein folding and
dynamics.•Machine learning is having a large impact in protein structure …

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 …

The function of complex biomolecular machines relies heavily on their conformational
changes. Investigating these functional conformational changes is therefore essential for …

Understanding chemical mechanisms requires estimating dynamical statistics such as
expected hitting times, reaction rates, and committors. Here, we present a general …

Markov state models are widely used to describe and analyze protein dynamics based on
molecular dynamics simulations, specifically to extract functionally relevant characteristic …

Finding a low dimensional representation of data from long-timescale trajectories of
biomolecular processes, such as protein folding or ligand–receptor binding, is of …

The modeling of atomistic biomolecular simulations using kinetic models such as Markov
state models (MSMs) has had many notable algorithmic advances in recent years. The …

State-free reversible VAMPnets (SRVs) are a neural network-based framework capable of
learning the leading eigenfunctions of the transfer operator of a dynamical system from …

Molecular dynamics (MD) simulation has become a powerful tool to investigate the structure-
function relationship of proteins and other biological macromolecules at atomic resolution …