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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.08859 |
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| _version_ | 1866916750791016448 |
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| author | Wang, Fanmeng Guo, Wentao Ou, Qi Wang, Hongshuai Lin, Haitao Xu, Hongteng Gao, Zhifeng |
| author_facet | Wang, Fanmeng Guo, Wentao Ou, Qi Wang, Hongshuai Lin, Haitao Xu, Hongteng Gao, Zhifeng |
| contents | Polymer conformation generation is a critical task that enables atomic-level studies of diverse polymer materials. While significant advances have been made in designing conformation generation methods for small molecules and proteins, these methods struggle to generate polymer conformations due to their unique structural characteristics. Meanwhile, the scarcity of polymer conformation datasets further limits the progress, making this important 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. 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, and then generating their orientation transformations via a diffusion model to assemble them 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 outperforms existing conformation generation methods, thus facilitating advancements in polymer modeling and simulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08859 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PolyConf: Unlocking Polymer Conformation Generation through Hierarchical Generative Models Wang, Fanmeng Guo, Wentao Ou, Qi Wang, Hongshuai Lin, Haitao Xu, Hongteng Gao, Zhifeng Soft Condensed Matter Artificial Intelligence Polymer conformation generation is a critical task that enables atomic-level studies of diverse polymer materials. While significant advances have been made in designing conformation generation methods for small molecules and proteins, these methods struggle to generate polymer conformations due to their unique structural characteristics. Meanwhile, the scarcity of polymer conformation datasets further limits the progress, making this important 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. 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, and then generating their orientation transformations via a diffusion model to assemble them 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 outperforms existing conformation generation methods, thus facilitating advancements in polymer modeling and simulation. |
| title | PolyConf: Unlocking Polymer Conformation Generation through Hierarchical Generative Models |
| topic | Soft Condensed Matter Artificial Intelligence |
| url | https://arxiv.org/abs/2504.08859 |