ArticleJournal of computational chemistry2014
Comparing distance metrics for rotation using the k-nearest neighbors algorithm for entropy estimation.
Article in Journal of computational chemistry, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Eye Tracking and Motion Data Predict Endoscopic Sinus Surgery Skill.The Laryngoscope · 2023Article
- Thermodynamic Compensation in Peptides Following Liquid-Liquid Phase Separation.The journal of physical chemistry. B · 2021Article
- The quaternion-based spatial-coordinate and orientation-frame alignment problems.Acta crystallographica. Section A, Foundations and advances · 2020Article
- Engineering the entropy-driven free-energy landscape of a dynamic nanoporous protein assembly.Nature chemistry · 2018Article
- Dynamics and Thermodynamics of Transthyretin Association from Molecular Dynamics Simulations.BioMed research international · 2018Article
- Studying the role of cooperative hydration in stabilizing folded protein states.Journal of structural biology · 2016Article
- Solvation thermodynamic mapping of molecular surfaces in AmberTools: GIST.Journal of computational chemistry · 2016Article
- Spatial analysis and quantification of the thermodynamic driving forces in protein-ligand binding: binding site variability.Journal of the American Chemical Society · 2015Article
- Quantifying the entropy of binding for water molecules in protein cavities by computing correlations.Biophysical journal · 2015Article
- Distance-Based Configurational Entropy of Proteins from Molecular Dynamics Simulations.PloS one · 2015Article
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1 author.
Funding
Abstract
Distance metrics facilitate a number of methods for statistical analysis. For statistical mechanical applications, it is useful to be able to compute the distance between two different orientations of a molecule. However, a number of distance metrics for rotation have been employed, and in this study, we consider different distance metrics and their utility in entropy estimation using the k-nearest neighbors (KNN) algorithm. This approach shows a number of advantages over entropy estimation using a histogram method, and the different approaches are assessed using uniform randomly generated data, biased randomly generated data, and data from a molecular dynamics (MD) simulation of bulk water. The results identify quaternion metrics as superior to a metric based on the Euler angles. However, it is demonstrated that samples from MD simulation must be independent for effective use of the KNN algorithm and this finding impacts any application to time series data.
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