Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics
This work addresses the problem of simulating biological processes at larger scales for researchers in computational biology and chemistry, offering a simplified training setup compared to previous methods.
The paper tackled the challenge of accurately learning coarse-grained force fields for molecular dynamics without requiring force inputs during training, by training a diffusion generative model on protein structures, and demonstrated improved performance in reproducing equilibrium distributions and preserving dynamics like protein folding events in small- to medium-sized protein simulations.
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.