Generating Minimum Free Energy Paths With Denoising Diffusion Probabilistic Models

TL;DR

This paper introduces a novel approach combining denoising diffusion probabilistic models with the string method to efficiently generate minimum free energy transition paths in molecular systems, capturing solvent effects and high-dimensional configurations.

Contribution

It presents a new method that leverages DDPMs to accurately produce transition pathways, extending their application to complex molecular energy landscapes.

Findings

Successfully generated transition paths for Muller-Brown potential.

Accurately modeled alanine dipeptide transition pathways.

Implicitly captured solvent effects in molecular pathways.

Abstract

A method combining denoising diffusion probabilistic models (DDPMs) with the string method is presented to generate minimum free energy paths between metastable states in molecular systems. It has been demonstrated in recent work that DDPMs at low noise levels can approximate the gradient of the potential of mean force, allowing efficient sampling of high-dimensional configurational spaces. Building on this insight, it is shown here that DDPM-derived force fields accurately generate transition pathways for the analytical Muller-Brown potential and for the alanine dipeptide system at some range of noise levels for DDPMs, recovering the transition path and implicitly capturing solvent effects in the case of alanine dipeptide.