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Cited by 966

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Calculation of Protein-Ligand Binding Affinities

Gilson, Michael K., Zhou, Huan-Xiang·2007·Annual Review of Biophysics and Biomolecular Structure

Abstract

Accurate methods of computing the affinity of a small molecule with a protein are needed to speed the discovery of new medications and biological probes. This paper reviews physics-based models of binding, beginning with a summary of the changes in potential energy, solvation energy, and configurational entropy that influence affinity, and a theoretical overview to frame the discussion of specific computational approaches. Important advances are reported in modeling protein-ligand energetics, such as the incorporation of electronic polarization and the use of quantum mechanical methods. Recent calculations suggest that changes in configurational entropy strongly oppose binding and must be included if accurate affinities are to be obtained. The linear interaction energy (LIE) and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) methods are analyzed, as are free energy pathway methods, which show promise and may be ready for more extensive testing. Ultimately, major improvements in modeling accuracy will likely require advances on multiple fronts, as well as continued validation against experiment.

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Crossref · 10.1146/annurev.biophys.36.040306.132550

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Metadata

Authors
Gilson, M. K., & Zhou, H.-X.
Venue
Annual Review of Biophysics and Biomolecular Structure
Published
2007-06-01
Volume
36
Issue
1
Pages
21-42
ISSN
  • 1056-8700 (ISSN-L)
  • 1545-4266
OpenAlex
W2117620409
Cited by
966
References
5
FWCI
60.13
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Calculation of Protein-Ligand Binding Affinities. https://app.cochat.ai/papers/10.1146/annurev.biophys.36.040306.132550
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Gilson, M. K., & Zhou, H.-X. (2007). Calculation of Protein-Ligand Binding Affinities. Annual Review of Biophysics and Biomolecular Structure. https://doi.org/10.1146/annurev.biophys.36.040306.132550

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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.