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Adaptive partitioning by local density-peaks: An efficient density-based clustering algorithm for analyzing molecular dynamics trajectories.
- Source :
-
Journal of Computational Chemistry . 1/30/2017, Vol. 38 Issue 3, p152-160. 9p. - Publication Year :
- 2017
-
Abstract
- We present an efficient density-based adaptive-resolution clustering method APLoD for analyzing large-scale molecular dynamics (MD) trajectories. APLoD performs the k-nearest-neighbors search to estimate the density of MD conformations in a local fashion, which can group MD conformations in the same high-density region into a cluster. APLoD greatly improves the popular density peaks algorithm by reducing the running time and the memory usage by 2-3 orders of magnitude for systems ranging from alanine dipeptide to a 370-residue Maltose-binding protein. In addition, we demonstrate that APLoD can produce clusters with various sizes that are adaptive to the underlying density (i.e., larger clusters at low-density regions, while smaller clusters at high-density regions), which is a clear advantage over other popular clustering algorithms including k-centers and k-medoids. We anticipate that APLoD can be widely applied to split ultra-large MD datasets containing millions of conformations for subsequent construction of Markov State Models. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01928651
- Volume :
- 38
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Journal of Computational Chemistry
- Publication Type :
- Academic Journal
- Accession number :
- 119974493
- Full Text :
- https://doi.org/10.1002/jcc.24664