Polyformer: a generative framework for thermodynamic modeling of polymeric molecules

2026-04-14

AI Summary

Traditional structural biology methods typically predict only a single static conformation, making it difficult to capture the full thermodynamic ensemble of polymer conformations across different temperatures and their functional implications. To address this limitation, this work proposes Polyformer—the first generative framework capable of jointly leveraging molecular sequence and temperature to produce conformational ensembles that faithfully follow thermodynamic distributions. By integrating sequence information with thermodynamic variables such as temperature within a deep generative model, Polyformer enables unified modeling of protein folding, the complete thermodynamic conformational ensemble, and its temperature dependence. Validated on protein domains ranging from 50 to 111 residues, the generated conformations show strong agreement with molecular dynamics trajectories, significantly advancing the capability of dynamic structural prediction.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation
• Planning, Routing, and Scheduling: Temporal Planning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• thermodynamic modeling
• conformational ensemble
• polymeric molecules
• protein folding
• temperature dependence

Innovation

• generative modeling
• conformational ensemble
• thermodynamic modeling
• protein folding
• temperature-dependent dynamics