ArticleJournal of chemical information and modeling2025
SHINE: Deterministic Many-to-Many Clustering of Molecular Pathways.
Article in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Pathway representationDigital discovery · 2026Article
- Pathway Representation via Intrinsic Structural Medoids (PRISM): A Structural Mapping Approach to Clustering Molecular Pathways.bioRxiv : the preprint server for biology · 2026Article
- ScalingJournal of chemical information and modeling · 2026Article
- mdBIRCH for Fast, Scalable, Online Clustering of Molecular Dynamics Trajectories.Journal of chemical theory and computation · 2026Article
- Divide and Cluster: The DIVINE Framework for Deterministic Top-Down Analysis of Molecular Dynamics Trajectories.Journal of chemical information and modeling · 2026Article
- mdBIRCH for Fast, Scalable, Online Clustering of Molecular Dynamics Trajectories.bioRxiv : the preprint server for biology · 2025Article
- clusttraj: A Solvent-Informed Clustering Tool for Molecular Modeling.Journal of chemical theory and computation · 2025Article
- Weighted Ensemble Simulation: Advances in methods, software, and applications.Wiley interdisciplinary reviews. Computational molecular scienceArticle
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5 authors.
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Abstract
State-of-the-art molecular dynamics (MD) simulation methods can generate diverse ensembles of pathways for complex biological processes. Analyzing these pathways using statistical mechanics tools demands identifying key states that contribute to both the dynamic and equilibrium properties of the system. This task becomes especially challenging when analyzing multiple MD simulations simultaneously, a common scenario in enhanced sampling techniques like the weighted ensemble strategy. Here, we present a new module of the MDANCE package designed to streamline the analysis of pathway ensembles. This module integrates n-ary similarity, cheminformatics-inspired tools, and hierarchical clustering to improve analysis efficiency. We present the theoretical foundation behind this approach, termed Sampling Hierarchical Intrinsic N-ary Ensembles (SHINE), and demonstrate its application to simulations of alanine dipeptide and adenylate kinase.
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Registered trials
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