Hierarchical geometric deep learning enables scalable analysis of molecular dynamics
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909947484176384 |
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| author | Pengmei, Zihan Guo, Spencer C. Lorpaiboon, Chatipat Dinner, Aaron R. |
| author_facet | Pengmei, Zihan Guo, Spencer C. Lorpaiboon, Chatipat Dinner, Aaron R. |
| contents | Molecular dynamics simulations can generate atomically detailed trajectories of complex systems, but analyzing these dynamics can be challenging when systems lack well-established quantitative descriptors (features). Graph neural networks (GNNs) in which messages are passed between nodes that represent atoms that are spatial neighbors promise to obviate manual feature engineering, but the use of GNNs with biomolecular systems of more than a few hundred residues has been limited in the context of analyzing dynamics by both difficulties in capturing the details of long-range interactions with message passing and the memory and runtime requirements associated with large graphs. Here, we show how local information can be aggregated to reduce memory and runtime requirements without sacrificing atomic detail. We demonstrate that this approach opens the door to analyzing simulations of protein-nucleic acid complexes with thousands of residues on single GPUs within minutes. For systems with hundreds of residues, for which there are sufficient data to make quantitative comparisons, we show that the approach improves performance and interpretability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_06520 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hierarchical geometric deep learning enables scalable analysis of molecular dynamics Pengmei, Zihan Guo, Spencer C. Lorpaiboon, Chatipat Dinner, Aaron R. Machine Learning Statistical Mechanics Computational Physics Data Analysis, Statistics and Probability Molecular dynamics simulations can generate atomically detailed trajectories of complex systems, but analyzing these dynamics can be challenging when systems lack well-established quantitative descriptors (features). Graph neural networks (GNNs) in which messages are passed between nodes that represent atoms that are spatial neighbors promise to obviate manual feature engineering, but the use of GNNs with biomolecular systems of more than a few hundred residues has been limited in the context of analyzing dynamics by both difficulties in capturing the details of long-range interactions with message passing and the memory and runtime requirements associated with large graphs. Here, we show how local information can be aggregated to reduce memory and runtime requirements without sacrificing atomic detail. We demonstrate that this approach opens the door to analyzing simulations of protein-nucleic acid complexes with thousands of residues on single GPUs within minutes. For systems with hundreds of residues, for which there are sufficient data to make quantitative comparisons, we show that the approach improves performance and interpretability. |
| title | Hierarchical geometric deep learning enables scalable analysis of molecular dynamics |
| topic | Machine Learning Statistical Mechanics Computational Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2512.06520 |