Enhanced sampling algorithms have emerged as powerful methods to extend the utility of
molecular dynamics simulations and allow the sampling of larger portions of the …

Computational techniques have been applied in the drug discovery pipeline since the
1980s. Given the low computational resources of the time, the first molecular modeling …

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 …

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 …

Markov (state) models (MSMs) and related models of molecular kinetics have recently
received a surge of interest as they can systematically reconcile simulation data from either …

Inspired by the success of deep learning techniques in the physical and chemical sciences,
we apply a modification of an autoencoder type deep neural network to the task of …

An increased synergy between experimental and theoretical investigations in
heterogeneous catalysis has become apparent during the last decade. Experimental work …

In modern-day simulations of many-body systems, much of the computational complexity is
shifted to the identification of slowly changing molecular order parameters called collective …

Dynamical reweighting techniques aim to recover the correct molecular dynamics from a
simulation at a modified potential energy surface. They are important for unbiasing …

Efforts to combine theory and experiment to advance our knowledge of molecular processes
relevant to biophysics have been considerably enhanced by the contribution of statistical …