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Abstract
Background: For > 30 years, the estrogen receptor (ER) has been the most important biomarker in breast cancer, principally because of its role in indicating the potential of patients to benefit from endocrine therapy. The search for modulators of ER (selective estrogen receptor modulators) through the use of computational methods such as virtual screening (VS) has redefined the area. Objective: We demonstrate how this receptor has become a key target in the computational (docking and scoring, pharmacophore) arena for algorithm development and validation. The use of quantitative structure–activity relationship for estimation of binding affinity to ER is also discussed, and finally all examples of lead identification through VS are exemplified using several VS campaigns carried out to identify environmental endocrine disruptors. Method: This review comprehensively details all current applications of virtual screening to the estrogen receptor and demonstrates how its use has pushed the boundaries of VS in general. Conclusion: The widespread application of the estrogen receptor to VS has allowed identification of numerous pitfalls within the process flow of VS such as library generation, correct validation procedures for docking/scoring functions, and inclusion of receptor flexibility.