mdBIRCH for Fast, Scalable, Online Clustering of Molecular Dynamics Trajectories
Woody Santos, J. B.; Chen, L.; Miranda Quintana, R. A.
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We present mdBIRCH, an online clustering method that adapts the BIRCH CF-tree to molecular dynamics (MD) data by using a merge test calibrated directly to RMSD. Each arriving frame is routed to the nearest centroid and added only if the post-merge radius computed from the cluster feature remains within a user-supplied threshold. This keeps the average deviation to each cluster centroid bounded as the cluster grows and preserves a simple interpretation of resolution in physical units. We evaluate mdBIRCH on a {beta}-heptapeptide and the HP35 system. We propose two protocols to make the threshold selection easier: (a) RMSD-anchored runs that use controlled structural edits to define interpretable operating points and (b) blind sweep that tracks how cluster count, occupancy, and coverage change with the threshold. In both systems, increasing the threshold reduces the number of clusters, concentrates coverage in high-occupancy states, and broadens within-cluster RMSD distributions. Furthermore, because decisions rely only on cluster summaries, mdBIRCH completely avoids the need for pairwise distance matrices, scales near-linearly with the number of frames on standard hardware, and naturally supports incremental operation. The method offers a practical combination of speed and interpretability for large-scale trajectory analysis.
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