Can We Extract Physics-like Energies from Generative Protein Diffusion Models?

Sudeep Sarma, Harrison Truscott, Da Xu, Kendall Reid, Lee-Shin Chu, Jacky Chen, Jeffrey J Gray

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In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Sudeep SarmaDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0000-0002-4642-7689

Harrison TruscottDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0009-0004-7045-980X

Da XuDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0000-0002-3879-039X

Kendall ReidBioinformatics Program, Center for Biotechnology Education, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0000-0002-2105-6471

Lee-Shin ChuDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0000-0001-8315-642X

Jacky ChenCMU-Pitt Computational Biology, Dept. of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA.

Jeffrey J GrayDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0000-0001-6380-2324

Funding

Prediction of the Structures of Protein ComplexesR35GM141881 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI JEFFREY J GRAY · 2021 to 2026

$7.6M

NIGMS NIH HHS R35 GM141881

6 · The paper itself

Abstract

Diffusion models have emerged as the state-of-the-art method in generative artificial intelligence (AI) and have shown great success in image synthesis, video generation, molecular design, and protein structure prediction. For biophysical problems, such as protein folding and association, a fundamental question in diffusion-based methods is how their learned functions correspond to thermodynamics. In this paper, we study diffusion models through the lens of theoretical biophysics, analyzing their underlying formulation of potentials and exploring their applications in scoring protein interactions. We develop simple theories rooted in statistical physics that relate thermodynamic potentials to the negative log of the probability of observing a system in a particular state. We include dimensional analysis of diffusion model equations and a table mapping AI and physics jargon. We then test a diffusion model's ability to capture learned energies as negative log-likelihood values,

Identifiers

PMID41394579

DOI10.1101/2025.11.28.690021

PMCPMC12697549

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