Simulations meet machine learning in structural biology

Curr Opin Struct Biol. 2018 Apr:49:139-144. doi: 10.1016/j.sbi.2018.02.004. Epub 2018 Feb 21.

Abstract

Classical molecular dynamics (MD) simulations will be able to reach sampling in the second timescale within five years, producing petabytes of simulation data at current force field accuracy. Notwithstanding this, MD will still be in the regime of low-throughput, high-latency predictions with average accuracy. We envisage that machine learning (ML) will be able to solve both the accuracy and time-to-prediction problem by learning predictive models using expensive simulation data. The synergies between classical, quantum simulations and ML methods, such as artificial neural networks, have the potential to drastically reshape the way we make predictions in computational structural biology and drug discovery.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Computational Biology / methods
  • Computer Simulation*
  • Machine Learning*
  • Models, Molecular*
  • Molecular Dynamics Simulation
  • Quantitative Structure-Activity Relationship*