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 …

Molecular dynamics simulations provide a mechanistic description of molecules by relying
on empirical potentials. The quality and transferability of such potentials can be improved …

Coarse graining enables the investigation of molecular dynamics for larger systems and at
longer timescales than is possible at an atomic resolution. However, a coarse graining …

Generation and analysis of time-series data is relevant to many quantitative fields ranging
from economics to fluid mechanics. In the physical sciences, structures such as metastable …

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

Modern computational chemistry has reached a stage at which massive exploration into
chemical reaction space with unprecedented resolution with respect to the number of …

The aim of molecular coarse-graining approaches is to recover relevant physical properties
of the molecular system via a lower-resolution model that can be more efficiently simulated …

Transporters are targeted by endogenous metabolites and exogenous molecules to reach
cellular destinations, but it is generally not understood how different substrate classes …

Proteins are dynamic molecules that can transition between a potentially wide range of
structures comprising their conformational ensemble. The nature of these conformations and …