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

The development of enhanced sampling methods has greatly extended the scope of
atomistic simulations, allowing long-time phenomena to be studied with accessible …

Highlights•Recent successes of artificial intelligence (AI) and machine learning (ML)
techniques can be leveraged to obtain quantitative insights into how intrinsically disordered …

Identifying a reduced set of collective variables is critical for understanding atomistic
simulations and accelerating them through enhanced sampling techniques. Recently …

The study of the rare transitions that take place between long lived metastable states is a
major challenge in molecular dynamics simulations. Many of the methods suggested to …

A significant challenge faced by atomistic simulations is the difficulty, and often impossibility,
to sample the transitions between metastable states of the free-energy landscape …

Analyzing large volumes of high-dimensional data requires dimensionality reduction: finding
meaningful low-dimensional structures hidden in their high-dimensional observations. Such …

Importance-sampling algorithms leaning on the definition of a model reaction coordinate
(RC) are widely employed to probe processes relevant to chemistry and biology alike …

Over the last few decades, enhanced sampling methods have been continuously improved.
Here, we exploit this progress and propose a modular workflow for blind reaction discovery …

Multiscale modeling has a long history of use in structural biology, as computational
biologists strive to overcome the time-and length-scale limits of atomistic molecular …