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mdBIRCH用于快速,可扩展,在线聚类的分子动力学轨迹

Jherome Brylle Woody Santos¹, Lexin Chen¹, Ramón Alain Miranda-Quintana¹

¹Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida, 32611, USA.

bioRxiv : the preprint server for biology

|November 24, 2025

概括

mdBIRCH使用适应的BIRCH算法高效地集群分子动力学数据. 它通过限制偏差和避免距离矩阵,为大型轨迹分析提供速度和可解释性.

科学领域:

计算化学是一种计算化学.
生物物理学的生物物理.
数据科学是数据科学.

背景情况:

分子动力学 (MD) 模拟产生大型数据集.
对MD轨迹的有效分析对于理解蛋白质动态至关重要.
现有的集群方法可能会与MD数据的规模和性质作斗争.

研究的目的:

介绍mdBIRCH,一个用于分子动力学数据的在线聚类方法.
适应BIRCH集群算法,以实现高效和可解释的轨迹分析.
为 MD 数据集群中的值选择提供实用工具.

主要方法:

使用适应的BIRCH CF树与RMSD校准的合并测试.
将传入的路由到最近的中心点,并根据用户定义的半径值添加它们.
采用RMSD定运行和盲目扫描协议来选择值.

主要成果:

mdBIRCH 随着数的增加而近乎线性地缩放,避免了对式距离矩阵.
提高门将减少集群数量,并将覆盖范围集中在高占用率州.
该方法保留了物理单位分辨率的解释性.

结论:

mdBIRCH为大规模分子动力学轨迹分析提供了一种快速和可解释的方法.
在线聚类方法适用于增量数据处理.
拟议的门选择协议简化了mdBIRCH.的应用.

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