Virtual screening of the estrogen receptor
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".