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A Computationally Based Identification Algorithm for Estrogen Receptor Ligands: Part 1. Predicting hERα Binding Affinity
Toxicological Sciences (2000)
  • Steven P. Bradbury
  • V. Kamenska
  • P. Schmieder
  • G. Ankley
  • O. Mekenyan

The common reactivity pattern (COREPA) approach is a 3-dimensional, quantitative structure activity relationship (3-D QSAR) technique that permits identification and quantification of specific global and local stereoelectronic characteristics associated with a chemical’s biological activity. It goes beyond conventional 3-D QSAR approaches by incorporating dynamic chemical conformational flexibility in ligand-receptor interactions. The approach provides flexibility in screening chemical data sets in that it helps establish criteria for identifying false positives and false negatives, and is not dependent upon a predetermined and specified toxicophore or an alignment of conformers to a lead compound. The algorithm was recently used to screen chemical data sets for rat androgen receptor binding affinity. To further explore the potential application of the algorithm in establishing reactivity patterns for human estrogen receptor a (hERa) binding affinity, the stereoelectronic requirements associated with the binding affinity of 45 steroidal and nonsteroidal ligands to the receptor were defined. Reactivity patterns for relative hERa binding affinity (RBA; 17b-estradiol 5 100%) were established based on global nucleophilicity, interatomic distances between electronegative heteroatoms, and electron donor capability of heteroatoms. These reactivity patterns were used to establish descriptor profiles for identifying and ranking compounds with RBA of > 150%, 100– 10%, 10–1%, and 1–0.1%. Increasing specificity of reactivity patterns was detected for ligand data sets with RBAs above 10%. Using the results of this analysis, an exploratory expert system was developed for use in ranking relative ER binding affinity potential for large chemical data sets.

  • structure activity relationships,
  • expert systems,
  • human estrogen relative binding affinity,
  • estrogen receptor ligands
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Citation Information
Steven P. Bradbury, V. Kamenska, P. Schmieder, G. Ankley, et al.. "A Computationally Based Identification Algorithm for Estrogen Receptor Ligands: Part 1. Predicting hERα Binding Affinity" Toxicological Sciences Vol. 58 (2000)
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