Deep signature: characterization of large-scale molecular dynamics

Conference item

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.

Authors: Qin, T; Zhu, M; Li, C; Lyons, T; Yan, H

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: OpenReview
• Host title: Proceedings of the International Conference on Learning Representations 2025 (ICLR 2025)
• Pages: 95009-95026
• Publication date: 2025-01-22
• Event title: 13th International Conference on Learning Representations (ICLR 2025)
• Event location: Singapore
• Event website: https://iclr.cc/Conferences/2025
• Event start date: 2025-04-24
• Event end date: 2025-04-28
• DOI: 10.48550/arxiv.2410.028
• Language: English