Identification of Novel Androgen Receptor Antagonists Using Structure- and Ligand-Based Methods
Bibliographic record
Abstract
Androgen receptor (AR) plays a critical role in the development and progression of prostate cancer (PCa). The AR hormone-binding site (HBS) is intensively studied and represents the target area for current antiandrogens including Bicalutamide and structurally related Enzalutamide. As resistance to antiandrogens invariably emerges in advanced prostate cancer, there exists a high medical need for the identification and development of novel AR antagonists of different chemotypes. Given the wealth of structural information on the AR in complex with a variety of ligands, we have applied an integrated structure- and ligand-based virtual screening methodology to identify novel AR antagonists. Virtual hits generated by a consensus voting approach were experimentally evaluated and resulted in the discovery of a number of structurally diverse submicromolar antagonists of the AR. In particular, one identified compound demonstrated anti-AR potency in vitro that is comparable to the clinically used Bicalutamide. These results set a ground for the development of novel classes of PCa drugs that are structurally different from current AR antagonists.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".