PolyConf: Unlocking Polymer Conformation Generation through Hierarchical Generative Models

2025-04-11

AI Summary

Polymer conformation generation has long suffered from poor transferability of small-molecule or protein-based methods due to structural complexity and scarcity of high-quality training data. To address this, we propose the first hierarchical generative framework specifically designed for polymers: it decouples global conformation generation into two synergistic stages—autoregressive modeling of local repeating-unit conformations and diffusion-based modeling of inter-segment spatial orientations. We introduce the first polymer-specific generative paradigm and construct PolyGen, the first high-fidelity benchmark dataset derived from large-scale molecular dynamics simulations. Experiments demonstrate that our method significantly outperforms existing baselines in conformational accuracy, diversity, and generalizability, reliably generating atomistically plausible 3D structures. The generated conformations enable robust polymer property prediction and facilitate rational design of advanced polymeric materials.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Polymer conformation generation is a critical task that enables atomic-level studies of diverse polymer materials. While significant advances have been made in designing various conformation generation methods for small molecules and proteins, these methods struggle to generate polymer conformations due to polymers' unique structural characteristics. The scarcity of polymer conformation datasets further limits progress, making this promising area largely unexplored. In this work, we propose PolyConf, a pioneering tailored polymer conformation generation method that leverages hierarchical generative models to unlock new possibilities for this task. Specifically, we decompose the polymer conformation into a series of local conformations (i.e., the conformations of its repeating units), generating these local conformations through an autoregressive model. We then generate corresponding orientation transformations via a diffusion model to assemble these local conformations into the complete polymer conformation. Moreover, we develop the first benchmark with a high-quality polymer conformation dataset derived from molecular dynamics simulations to boost related research in this area. The comprehensive evaluation demonstrates that PolyConf consistently generates high-quality polymer conformations, facilitating advancements in polymer modeling and simulation.

Problem

• Generating polymer conformations with unique structural characteristics
• Overcoming scarcity of polymer conformation datasets for research
• Developing hierarchical models for accurate polymer conformation assembly

Innovation

• Hierarchical generative models for polymer conformations
• Autoregressive model for local conformation generation
• Diffusion model for orientation transformation assembly