Molecular dynamics (MD) enables the study of physical systems with excellent
spatiotemporal resolution but suffers from severe timescale limitations. To address this …

Large-scale computational molecular models provide scientists a means to investigate the
effect of microscopic details on emergent mesoscopic behavior. Elucidating the relationship …

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 …

The weighted ensemble (WE) strategy has been demonstrated to be highly efficient in
generating pathways and rate constants for rare events such as protein folding and protein …

Antigen presentation by major histocompatibility complex (MHC) proteins is essential for
adaptive immunity. Prior to presentation, peptides need to be generated from proteins that …

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 …

Introduction: Molecular dynamics (MD) simulations can provide not only plentiful dynamical
structural information on biomacromolecules but also a wealth of energetic information …

Cryptic pockets expand the scope of drug discovery by enabling targeting of proteins
currently considered undruggable because they lack pockets in their ground state structures …

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 …