Polyformer: a generative framework for thermodynamic modeling of polymeric molecules

arXiv cs.LG / 4/17/2026

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Key Points

Polyformer is introduced as a generative framework that performs thermodynamic modeling for polymeric molecules rather than predicting a single static structure.
By taking a molecule’s sequence and temperature (or another thermodynamic variable) as inputs, Polyformer generates conformations that match the molecule’s thermodynamic conformational ensemble.
The approach is positioned as the first generative model addressing folding, ensemble formation, and ensemble changes with temperature in a unified way.
In tests on protein domains of 50–111 residues, Polyformer’s predictions show good agreement with Molecular Dynamics (MD) trajectories.
The work extends the structural-biology paradigm from “sequence → best conformation” to “sequence + thermodynamics → ensemble behavior,” supporting more realistic modeling of biomolecular function.

Abstract

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.