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.
@article{arxiv.2302.00600,
title = {Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics},
author = {Marloes Arts and Victor Garcia Satorras and Chin-Wei Huang and Daniel Zuegner and Marco Federici and Cecilia Clementi and Frank Noé and Robert Pinsler and Rianne van den Berg},
journal= {arXiv preprint arXiv:2302.00600},
year = {2023}
}