![[2602.19444] PIS: A Physics-Informed System for Accurate State Partitioning of $Aβ_{42}$ Protein Trajectories](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.19444] PIS: A Physics-Informed System for Accurate State Partitioning of $Aβ_{42}$ Protein Trajectories
Summary
The article presents PIS, a Physics-Informed System that enhances the state partitioning of $Aβ_{42}$ protein trajectories, crucial for understanding Alzheimer's disease mechanisms.
Why It Matters
This research addresses the limitations of current deep learning models in capturing subtle transitions in protein dynamics, which is vital for advancing Alzheimer's research. By integrating physical constraints, PIS offers a more reliable analytical tool for biological researchers, potentially leading to better insights into disease mechanisms.
Key Takeaways
- PIS integrates physical priors to improve state partitioning of $Aβ_{42}$ protein trajectories.
- The system enhances the interpretability of protein dynamics through dynamic monitoring.
- PIS outperforms existing models in capturing subtle state transitions.
- The tool provides biological researchers with robust analytical capabilities.
- An interactive platform allows for multi-dimensional validation of results.
Computer Science > Machine Learning arXiv:2602.19444 (cs) [Submitted on 23 Feb 2026] Title:PIS: A Physics-Informed System for Accurate State Partitioning of $Aβ_{42}$ Protein Trajectories Authors:Qianfeng Yu, Ningkang Peng, Yanhui Gu View a PDF of the paper titled PIS: A Physics-Informed System for Accurate State Partitioning of $A\beta_{42}$ Protein Trajectories, by Qianfeng Yu and 2 other authors View PDF HTML (experimental) Abstract:Understanding the conformational evolution of $\beta$-amyloid ($A\beta$), particularly the $A\beta_{42}$ isoform, is fundamental to elucidating the pathogenic mechanisms underlying Alzheimer's disease. However, existing end-to-end deep learning models often struggle to capture subtle state transitions in protein trajectories due to a lack of explicit physical constraints. In this work, we introduce PIS, a Physics-Informed System designed for robust metastable state partitioning. By integrating pre-computed physical priors, such as the radius of gyration and solvent-accessible surface area, into the extraction of topological features, our model achieves superior performance on the $A\beta_{42}$ dataset. Furthermore, PIS provides an interactive platform that features dynamic monitoring of physical characteristics and multi-dimensional result validation. This system offers biological researchers a powerful set of analytical tools with physically grounded interpretability. A demonstration video of PIS is available on this https URL. Subjects: Ma...
