Parallelized datasets have also driven experimental work by generating predictive models. For example, Folding@home data was used to identify and characterize novel cryptic pockets in a variety of infectious disease targets in bacteria and viruses . Accounting for the dynamics within the active site of enzymes substantially using parallel MD datasets improved the predictive ability of modern virtual screening technologies . Similar approaches have yielded valuable insights into biophysical phenomena such as the pH dependence of protein-protein interactions or allosteric activation of signaling proteins .