Virtual screening of the estrogen receptor

Area of Science:

• Computational chemistry and drug discovery
• Molecular biology and pharmacology

Background:

• Estrogen receptor (ER) is a critical biomarker in breast cancer, predicting response to endocrine therapy.
• Selective estrogen receptor modulators (SERMs) have redefined ER-targeted treatments.
• Computational methods, particularly virtual screening (VS), have revolutionized ER research.

Purpose of the Study:

• To review the applications of VS in targeting the estrogen receptor.
• To demonstrate how ER research has driven advancements in VS algorithm development and validation.
• To discuss quantitative structure-activity relationship (QSAR) for ER binding affinity estimation.

Main Methods:

• Comprehensive review of virtual screening techniques applied to the estrogen receptor.
• Analysis of VS campaigns for lead identification and environmental endocrine disruptor discovery.
• Discussion of docking, scoring, pharmacophore modeling, and QSAR methods.

Main Results:

• Virtual screening has been instrumental in identifying potential ER modulators.
• ER serves as a key target for computational methods, driving algorithm development.
• VS campaigns have successfully identified environmental endocrine disruptors.

Conclusions:

• The estrogen receptor is a valuable target for VS, pushing the boundaries of the technique.
• Widespread ER application in VS has revealed challenges in library generation and validation.
• Addressing receptor flexibility and validation protocols is crucial for effective VS.