Deep Signature: Characterization of Large-Scale Molecular Dynamics
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866915286844702720 |
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| author | Qin, Tiexin Zhu, Mengxu Li, Chunyang Lyons, Terry Yan, Hong Li, Haoliang |
| author_facet | Qin, Tiexin Zhu, Mengxu Li, Chunyang Lyons, Terry Yan, Hong Li, Haoliang |
| contents | Understanding protein dynamics are essential for deciphering protein functional mechanisms and developing molecular therapies. However, the complex high-dimensional dynamics and interatomic interactions of biological processes pose significant challenge for existing computational techniques. In this paper, we approach this problem for the first time by introducing Deep Signature, a novel computationally tractable framework that characterizes complex dynamics and interatomic interactions based on their evolving trajectories. Specifically, our approach incorporates soft spectral clustering that locally aggregates cooperative dynamics to reduce the size of the system, as well as signature transform that collects iterated integrals to provide a global characterization of the non-smooth interactive dynamics. Theoretical analysis demonstrates that Deep Signature exhibits several desirable properties, including invariance to translation, near invariance to rotation, equivariance to permutation of atomic coordinates, and invariance under time reparameterization. Furthermore, experimental results on three benchmarks of biological processes verify that our approach can achieve superior performance compared to baseline methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_02847 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Signature: Characterization of Large-Scale Molecular Dynamics Qin, Tiexin Zhu, Mengxu Li, Chunyang Lyons, Terry Yan, Hong Li, Haoliang Quantitative Methods Artificial Intelligence Understanding protein dynamics are essential for deciphering protein functional mechanisms and developing molecular therapies. However, the complex high-dimensional dynamics and interatomic interactions of biological processes pose significant challenge for existing computational techniques. In this paper, we approach this problem for the first time by introducing Deep Signature, a novel computationally tractable framework that characterizes complex dynamics and interatomic interactions based on their evolving trajectories. Specifically, our approach incorporates soft spectral clustering that locally aggregates cooperative dynamics to reduce the size of the system, as well as signature transform that collects iterated integrals to provide a global characterization of the non-smooth interactive dynamics. Theoretical analysis demonstrates that Deep Signature exhibits several desirable properties, including invariance to translation, near invariance to rotation, equivariance to permutation of atomic coordinates, and invariance under time reparameterization. Furthermore, experimental results on three benchmarks of biological processes verify that our approach can achieve superior performance compared to baseline methods. |
| title | Deep Signature: Characterization of Large-Scale Molecular Dynamics |
| topic | Quantitative Methods Artificial Intelligence |
| url | https://arxiv.org/abs/2410.02847 |