  Coarse-grained (CG) molecular dynamics enables the study of biological
processes at temporal and spatial scales that would be intractable at an
atomistic resolution. However, accurately learning a CG force field remains a
challenge. In this work, we leverage connections between score-based generative
models, force fields and molecular dynamics to learn a CG force field without
requiring any force inputs during training. Specifically, we train a diffusion
generative model on protein structures from molecular dynamics simulations, and
we show that its score function approximates a force field that can directly be
used to simulate CG molecular dynamics. While having a vastly simplified
training setup compared to previous work, we demonstrate that our approach
leads to improved performance across several small- to medium-sized protein
simulations, reproducing the CG equilibrium distribution, and preserving
dynamics of all-atom simulations such as protein folding events.
