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 …

Background Antivirals are needed to combat the COVID-19 pandemic, which is caused by
SARS-CoV-2. The clinically-proven protease inhibitor Camostat mesylate inhibits SARS …

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

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 …

Coarse-grained (CG) molecular dynamics enables the study of biological processes at
temporal and spatial scales that would be intractable at an atomistic resolution. However …

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 …

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 …

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