Using the structures from experiment (or models derived from experimental structural datasets) as a starting point, molecular dynamics (MD) simulations have the potential to provide atomistic-level detail to explain complex biological processes. These simulations compute the movements of atoms over time by integrating Newton's laws of motion over each atom. Thus, some may consider MD to act as a "computational microscope", allowing us to observe the different conformations a protein adopts . However, one should be careful when considering the time evolution between frames providing equilibrium samples. A perfect simulation would completely describe a protein's thermodynamic and kinetic behaviors at equilibrium. However, there are major limitations: (i) The accuracy of atomic parameters (aka "force fields") that are used to describe atomic interaction (a topic that is discussed extensively elsewhere ). (ii) Simulations take femtosecond-sized timesteps, making it expensive to gather data at biologically relevant timescales (microseconds to milliseconds). (iii) Analyzing large datasets with the high-dimensional conformational changes of biomolecules to generate biologically meaningful predictions remains a daunting task.