Recruiting machine learning methods for molecular simulations of proteins

Shriyaa Mittal, Diwakar Shukla

Research output: Contribution to journalArticlepeer-review


Molecular dynamics (MD) simulations are critical to understanding the movements of proteins in time. Yet, MD simulations are limited due to the availability of high-resolution protein structures, accuracy of the underlying force-field, computational expense, and difficulty in analysing big data-sets. Machine learning algorithms are now routinely used to circumvent many of these limitations and computational biophysicists are continuously making progress in developing novel applications. Here, we discuss some of these methods, varying from traditional dimensionality reduction approaches to more recent abstractions such as transfer learning and reinforcement learning, and how they have been used to deal with the challenges in MD. We conclude with the prospective issues in the application of machine learning methods in MD, to increase accuracy and efficiency of protein dynamics studies in general.

Original languageEnglish (US)
Pages (from-to)891-904
Number of pages14
JournalMolecular Simulation
Issue number11
StatePublished - Jul 24 2018


  • Markov state model
  • Protein dynamics
  • machine learning
  • reinforcement learning
  • transfer learning

ASJC Scopus subject areas

  • Chemistry(all)
  • Information Systems
  • Modeling and Simulation
  • Chemical Engineering(all)
  • Materials Science(all)
  • Condensed Matter Physics

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