One major opportunity in generating parallel simulations lies in their potential to create the next generation of datasets that will be essential for training frontier models that predict protein dynamics and generate conformational ensembles . The inherent parallelism of the approach in this chapter allows for the simultaneous study of mutiple protein topologies and constructs. This breadth of data provides not only insight into the protein's conformational landscape but also offers a rich source of training data for machine learning models aiming to predict protein behavior at different timescales. Furthermore, by saving forces per atom during these simulations, the data can be utilized to inform coarse-grained models, accelerating simulations while maintaining critical details . By incorporating protein-ligand and protein-protein complexes, these datasets can provide training data for ML models predicting protein-protein interactions (PPIs) and inhibitor binding poses. When paired with quantum mechanical (QM) datasets, parallel simulations can provide conformational data to augment models that predict molecular structural properties with greater accuracy. This combination of data-rich simulations and detailed force calculations will be invaluable for developing machine learning force fields that can better predict binding affinities, molecular interactions, and other critical properties in structural biology and drug discovery .