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Polyformer: a generative framework for thermodynamic modeling of polymeric molecules

arXiv 2604.14241 ↗First posted 15 Apr 2026q-bio.BM cond-mat.stat-mech cs.LGcited by 0

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Abstract (arXiv)

The classic paradigm of structural biology is that the sequence of a biomolecule (protein, nucleic acid, lipid, etc) determines its conformation (shape) which determines its biological function. Protein folding programs like AlphaFold address this paradigm by predicting the single best conformation given a sequence that defines the molecule. However, biomolecules are not static structures, and their conformational ensemble determines their function. We present the Polyformer -- a generative framework for thermodynamic modeling of polymeric molecules. Given the sequence and temperature (or another thermodynamic variable), the Polyformer generates conformations faithful to the molecule's thermodynamic conformational ensemble. It is the first generative model that solves three problems simultaneously: how does a molecule fold, what is its conformational ensemble, and how does the conformational ensemble change as we change physical temperature. As a concrete test case, we apply Polyformer to protein domains with 50-111 residues and report good agreement of model predictions to Molecular Dynamics (MD) trajectories.

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References · 7

  1. 2020Denoising Diffusion Implicit Modelscited by 9,655
  2. 2022Scalable Diffusion Models with Transformerscited by 5,931
  3. 2023Efficient Diffusion Training via Min-SNR Weighting Strategycited by 242
  4. 2024AlphaFold Meets Flow Matching for Generating Protein Ensemblescited by 105
  5. 2021Learnable Fourier Features for Multi-Dimensional Spatial Positional Encodingcited by 93
  6. 2017Unsupervised Generative Modeling Using Matrix Product Statescited by 87
  7. 2025SimpleFold: Folding Proteins is Simpler than You Thinkcited by 16

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