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 …

Machine learning (ML) is transforming all areas of science. The complex and time-
consuming calculations in molecular simulations are particularly suitable for an ML …

G protein-coupled receptors (GPCRs) are the most common proteins targeted by approved
drugs. A complete mechanistic elucidation of large-scale conformational transitions …

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 …

Atomistic or ab initio molecular dynamics simulations are widely used to predict
thermodynamics and kinetics and relate them to molecular structure. A common approach to …

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 …

While a complete understanding of organic semiconductor (OSC) design principles remains
elusive, computational methods─ ranging from techniques based in classical and quantum …

Ensemble docking corresponds to the generation of an" ensemble" of drug target
conformations in computational structure-based drug discovery, often obtained by using …

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 …

Molecular simulations, which simulate the motions of particles according to fundamental
laws of physics, have been applied to a wide range of fields from physics and materials …