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BitClust: Fast Geometrical Clustering of Long Molecular Dynamics Simulations

2019; American Chemical Society; Volume: 60; Issue: 2 Linguagem: Inglês

10.1021/acs.jcim.9b00828

1549-960X

Roy González‐Alemán, David Hernández‐Castillo, Alejandro Rodríguez-Serradet, Julio Caballero, Erix W. Hernández‐Rodríguez, Luís A. Montero,

Machine Learning in Bioinformatics

The growing computational capacity allows the investigation of large biomolecular systems by increasingly extensive molecular dynamics simulations. The resulting huge trajectories demand efficient partition methods to discern relevant structural dissimilarity. Clustering algorithms are available to address this task, but their implementations still need to be improved to gain in computational speed and to reduce the consumption of random access memory. We propose the BitClust code which, based on a combination of Python and C programming languages, performs fast structural clustering of long molecular trajectories. BitClust takes advantage of bitwise operations applied to a bit-encoded pairwise similarity matrix. Our approach allowed us to process a half-million frame trajectory in 6 h using less than 35 GB, a task that is not affordable with any of the similar alternatives.