At the same time, ensemble-averaging also reduces the sensitivity to inaccuracies in the force field or scoring functions. MD-based refinement via ensemble-averaging has been one of the most successful strategies for consistently refining both global and local structure features. With state-of-the-art MD-based refinement protocols, it has become possible to consistently bring initial models significantly closer to their experimental structures for most protein structures. There are examples, where refined models reached root-mean-square-deviations of Cα atoms (Cα-RMSD) of around 1 Å from the native structures as demonstrated during CASP13. In the past, refinement has been mostly applied to models built based on homology modeling because only those had sufficiently high initial model qualities. With the advances in structure prediction based on machine-learning methods, the resulting models are also good starting points for refinement. Recently, it was shown that refinement is also able to improve these models. MD-based refinement was in particular able to improve the structure packing between sidechains as well as loop structures.