Abstract
Both machine learning (ML) and molecular dynamics (MD) techniques have benefited substantially from the growing accessibility to computational resources, enabling more sophisticated ML models and more extensive MD simulations. Over recent years, the use of ML and MD methods has become increasingly prevalent in computer-aided drug discovery. To exploit the synergies between these methodologies, researchers have started to explore approaches that combine ML techniques and MD simulations. We start this chapter with a brief introduction of the basics of MD simulations as well as ML applications in drug discovery. Next, three broad ways of marrying MD with ML are discussed: (1) use of ML to parameterise systems for MD simulations with increased accuracy, (2) use of ML to improve sampling efficiency during MD simulations, and (3) use of ML to post-process MD trajectories in order to gain quantitative insights into molecular processes. We conclude the chapter with our perspective on areas for further development of this emerging interdisciplinary field.
Published Version
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