How Combining Molecular Dynamics With Machine Learning Can Reap Benefits

Molecular dynamics has long being seen as a computer simulation method used for studying physical movements of atoms and molecules interacting with each other and giving a view of dynamic evolution of the system. Deemed to be important for a routine study of macromolecules and their environments, molecular dynamics is now being combined with machine learning to get results in various nascent areas. The idea of combining molecular dynamics with ML dates back to 2008 when they were combined to improve the protein function recognition of a molecule. They treated molecules as dynamic entities and improved the ability of structure-based function prediction methods to specify possible functional sites. Since then it has been used for various functionalities including creation of hyper predictive computer models for drug discovery and simulation of infrared spectra, among others.