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CodeEntropy: A Python Package for Multiscale Cell Correlation Entropy Estimation from Molecular Dynamics Simulations Cover

CodeEntropy: A Python Package for Multiscale Cell Correlation Entropy Estimation from Molecular Dynamics Simulations

Open Access
|Jul 2026

Figures & Tables

Figure 1

Overview of the CodeEntropy workflow showing static setup, conformational state construction, frame-level statistical accumulation, and final entropy evaluation.

Shared data model
JSON output
Figure 2

Command-line interface splash screen displayed during program initialisation, showing software identity and execution context.

Figure 3

Example runtime configuration loaded from a YAML file, illustrating user-defined analysis parameters and workflow options.

Figure 4

Terminal output showing aggregated entropy results, including total and component contributions computed from the accumulated statistical model.

Figure 5

Residue-level entropy decomposition presented as tabulated terminal output, illustrating how total entropy contributions are distributed across molecular components.

Figure 6

Entropy of 49 liquids from CodeEntropy versus experiment. The dashed line represents perfect agreement. Error bars are negligible.

Figure 7

CodeEntropy binding Gibbs energies ΔG (blue) versus experiment (green) for the host-guest systems in the SAMPL8 challenge.

DOI: https://doi.org/10.5334/jors.741 | Journal eISSN: 2049-9647
Language: English
Page range: 55 - 55
Submitted on: May 13, 2026
Accepted on: Jul 7, 2026
Published on: Jul 20, 2026
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services

© 2026 Harry Swift, Jas Kalayan, Ioana A. Papa, Sarah K. Fegan, Richard H. Henchman, Sarah A. Harris, James Gebbie-Rayet, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.