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An Alternative Method for the Evaluation of Docking Performance: RSR vs RMSD

Yusuf, Dilmurat, Davis, Andrew M., Kleywegt, Gerard J., Schmitt, Stefan·2008·Journal of Chemical Information and Modeling

Abstract

A new assessment criterion for docking poses is proposed in which experimental electron density is taken into account when evaluating the ability of docking programs to reproduce experimentally observed binding modes. Three docking programs (Gold, Glide, and Fred) were used to generate poses for a set of 88 protein-ligand complexes for which the crystal structure is known. The new criterion is based on the real space R-factor (RSR), which measures how well a group of atoms-in our case the ligand-fits the experimental electron density by comparing that density to the expected density, calculated from the model (i.e., the predicted ligand pose). The RSR-based measure is compared to the traditional criterion, the root-mean-square distance (RMSD) between the docking pose and the binding configuration in the crystallographic model. The results highlight several shortcomings of the RMSD criterion that do not affect the RSR-based measure. Examples illustrate that the RSR-derived approach allows a more meaningful a posteriori assessment of docking methods and results. Practical implications for docking evaluations and for methodological development work in this field are discussed.

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Metadata

Authors
Yusuf, D., Davis, A. M., Kleywegt, G. J., & Schmitt, S.
Venue
Journal of Chemical Information and Modeling
Published
2008-07-01
Volume
48
Issue
7
Pages
1411-1422
ISSN
  • 1549-9596 (ISSN-L)
  • 1549-960X
OpenAlex
W1995952752
Cited by
331
References
5
FWCI
6.97
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: An Alternative Method for the Evaluation of Docking Performance: RSR vs RMSD. https://app.cochat.ai/papers/10.1021/ci800084x
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Yusuf, D., Davis, A. M., Kleywegt, G. J., & Schmitt, S. (2008). An Alternative Method for the Evaluation of Docking Performance: RSR vs RMSD. Journal of Chemical Information and Modeling. https://doi.org/10.1021/ci800084x

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