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arXiv · 2302.00600

Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics

Marloes Arts·Victor Garcia Satorras·Chin-Wei Huang·Daniel Zuegner·Marco Federici·Cecilia Clementi·Frank Noé·Robert Pinsler·Rianne van den Berg
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Abstract

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.

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Marloes Arts, Victor Garcia Satorras, Chin-Wei Huang, Daniel Zuegner, Marco Federici, Cecilia Clementi, Frank Noé, Robert Pinsler, Rianne van den Berg. 2023-02-01. Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics. https://arxiv.org/abs/2302.00600

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