The computational determination of binding modes for a ligand into a protein receptor is much more successful than the prediction of relative binding affinities (RBAs) for a set of ligands. Here we consider the binding of a set of 26 synthetic A-CD ligands into the estrogen receptor ERα. We show that the MOE default scoring function (London dG) used to rank the docked poses leads to a negligible correlation with experimental RBAs. However, switching to an energy-based scoring function, using a multiple linear regression to fit experimental RBAs, selecting top-ranked poses and then iteratively repeating this process leads to exponential convergence in 4-7 iterations and a very strong correlation. The method is robust, as shown by various validation tests. This approach may be of general use in improving the quality of predicted binding affinities.

Additional Metadata
Keywords Docking, Estrogen receptor, Iterative rescoring, Scoring
Persistent URL dx.doi.org/10.1007/s10822-013-9670-6
Journal Journal of Computer-Aided Molecular Design
Citation
Wright, J.S, Anderson, J.M. (James M.), Shadnia, H. (Hooman), Durst, T. (Tony), & Katzenellenbogen, J.A. (John A.). (2013). Experimental versus predicted affinities for ligand binding to estrogen receptor: Iterative selection and rescoring of docked poses systematically improves the correlation. Journal of Computer-Aided Molecular Design, 27(8), 707–721. doi:10.1007/s10822-013-9670-6