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Cited by 562

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An information theory model of hydrophobic interactions.

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Hummer, G, Garde, S, García, A E, Pohorille, A, Pratt, L R·1996·Proceedings of the National Academy of Sciences

Abstract

A molecular model of poorly understood hydrophobic effects is heuristically developed using the methods of information theory. Because primitive hydrophobic effects can be tied to the probability of observing a molecular-sized cavity in the solvent, the probability distribution of the number of solvent centers in a cavity volume is modeled on the basis of the two moments available from the density and radial distribution of oxygen atoms in liquid water. The modeled distribution then yields the probability that no solvent centers are found in the cavity volume. This model is shown to account quantitatively for the central hydrophobic phenomena of cavity formation and association of inert gas solutes. The connection of information theory to statistical thermodynamics provides a basis for clarification of hydrophobic effects. The simplicity and flexibility of the approach suggest that it should permit applications to conformational equilibria of nonpolar solutes and hydrophobic residues in biopolymers.

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Metadata

Authors
Hummer, G., Garde, S., García, A. E., Pohorille, A., & Pratt, L. R.
Venue
Proceedings of the National Academy of Sciences
Published
1996-08-20
Volume
93
Issue
17
Pages
8951-8955
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W1969010881
Cited by
562
References
0
FWCI
11.51
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: An information theory model of hydrophobic interactions. https://app.cochat.ai/papers/10.1073/pnas.93.17.8951
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Hummer, G., Garde, S., García, A. E., Pohorille, A., & Pratt, L. R. (1996). An information theory model of hydrophobic interactions. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.93.17.8951

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.