Highly parallel molecular simulations are also poised to augment cryo-electron microscopy (cryo-EM) data. Com-Chapter Figure . Future directions that can benefit from distributed computing approaches include (clockwise from left): Free energy calculations to compute the impact of mutations on drug binding (lower left), using autoencoders and flow-matching models to identify generalized feature spaces that define the functional states of a biomolecule and its variants (upper left), refining Cryo-EM structural classes and particles using MD simulations to rank populations (upper right), and using parallel coarse-grained simulations to access seconds and longer timescale processes such as aggregation of fibrils (lower right) bining simulations with cryo-EM data has already proven powerful for refining molecular structures and capturing dynamic processes . The next logical step is the parallelization of these simulations, which would allow the exploration of multiple conformational states and trajectories simultaneously, greatly enhancing the resolution and accuracy of the models derived from cryo-EM density maps. By running multiple simulations in parallel, one can better capture the heterogeneity inherent in biological molecules. A proof of concept for reweighting MD simulation data by cryo-EM images to produce an experimentally derived free energy landscape have been established for small systems that can be easily converged , but in practice applying this method to systems where converging MD simulations for the large biomolecules of interest will require highly parallel molecular simulations is where this method will pay off dividends. Using large parallel datasets and MSMs will augment reconstruction of high-resolution structural ensembles to generate unified landscape projections of state populations (Fig. ) .