Equilibrium Sampling in Biomolecular Simulation

TL;DR

This paper reviews various methods for equilibrium sampling in biomolecular simulations, emphasizing algorithm classification, hardware utilization, and the challenges in measuring sampling effectiveness over 30 years.

Contribution

It provides a comprehensive classification of sampling algorithms, highlighting the impact of hardware advancements and the need for standardized sampling measures.

Findings

Hardware-based approaches show clearer progress than algorithms.

Most algorithms share underlying key ideas.

Sampling measurement remains a challenge.

Abstract

Equilibrium sampling of biomolecules remains an unmet challenge after more than 30 years of atomistic simulation. Efforts to enhance sampling capability, which are reviewed here, range from the development of new algorithms to parallelization to novel uses of hardware. Special focus is placed on classifying algorithms -- most of which are underpinned by a few key ideas -- in order to understand their fundamental strengths and limitations. Although algorithms have proliferated, progress resulting from novel hardware use appears to be more clear-cut than from algorithms alone, partly due to the lack of widely used sampling measures.