Machine Learning in Molecular Dynamics Simulations of Biomolecular Systems

TL;DR

This paper reviews how machine learning enhances molecular dynamics simulations by identifying metastable states, analyzing data, accelerating sampling, and improving models, thereby advancing understanding of molecular mechanisms and aiding experimental comparisons.

Contribution

It provides a comprehensive overview of ML applications in MD simulations, highlighting recent advances and potential future directions.

Findings

ML improves identification of metastable states

ML accelerates sampling in MD simulations

ML enhances the accuracy of molecular models

Abstract

Machine learning (ML) has emerged as a pervasive tool in science, engineering, and beyond. Its success has also led to several synergies with molecular dynamics (MD) simulations, which we use to identify and characterize the major metastable states of molecular systems. Typically, we aim to determine the relative stabilities of these states and how rapidly they interchange. This information allows mechanistic descriptions of molecular mechanisms, enables a quantitative comparison with experiments, and facilitates their rational design. ML impacts all aspects of MD simulations -- from analyzing the data and accelerating sampling to defining more efficient or more accurate simulation models.