Machine learning approaches for analyzing and enhancing molecular dynamics simulations

Yihang Wang, João Marcelo Lamim Ribeiro, Pratyush Tiwary

Research output: Contribution to journalReview articlepeer-review

171 Scopus citations

Abstract

Molecular dynamics (MD) has become a powerful tool for studying biophysical systems, due to increasing computational power and availability of software. Although MD has made many contributions to better understanding these complex biophysical systems, there remain methodological difficulties to be surmounted. First, how to make the deluge of data generated in running even a microsecond long MD simulation human comprehensible. Second, how to efficiently sample the underlying free energy surface and kinetics. In this short perspective, we summarize machine learning based ideas that are solving both of these limitations, with a focus on their key theoretical underpinnings and remaining challenges.

Original languageEnglish
Pages (from-to)139-145
Number of pages7
JournalCurrent Opinion in Structural Biology
Volume61
DOIs
StatePublished - Apr 2020

Fingerprint

Dive into the research topics of 'Machine learning approaches for analyzing and enhancing molecular dynamics simulations'. Together they form a unique fingerprint.

Cite this