Divide and Cluster: The DIVINE Framework for Deterministic Top-Down Analysis of Molecular Dynamics Trajectories

Abstract

We present DIVIsive N-ary Ensembles (DIVINE), a deterministic, top-down clustering framework designed for molecular dynamics (MD) trajectories. DIVINE constructs a complete clustering hierarchy by recursively splitting clusters based on n-ary similarity principles, avoiding the need for O(N2) pairwise distance matrices. It supports multiple cluster selection criteria, including a weighted variance metric, and deterministic anchor initialization strategies such as NANI (N-ary Natural Initiation), ensuring reproducible and structurally meaningful partitions. Testing DIVINE up to a 305 s folding trajectory of the villin headpiece (HP35) revealed that it matched or exceeded the clustering quality of bisecting k-means while reducing runtime and eliminating stochastic variability. Its single-pass design enables efficient exploration of clustering resolutions without repeated executions. By combining scalability, interpretability, and determinism, DIVINE offers a robust and practical alternative to conventional MD clustering methods. DIVINE is publicly available as part of the MDANCE package: https://github.com/mqcomplab/MDANCE.