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

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 dimens

SUBMITTER: Sarma S 

PROVIDER: S-EPMC12697549 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature