Learning From Estrogen Receptor Antagonism: Structure‐Based Identification of Novel Antiandrogens Effective Against Multiple Clinically Relevant Androgen Receptor Mutants
Bibliographic record
Abstract
Current treatment strategy for advanced prostate cancer is to suppress androgen receptor (AR) by castration and antiandrogens. However, several clinically relevant AR mutations cause insensitivity to current antiandrogens and convert them into agonists. We aim to identify full AR antagonists even for AR mutants. As crystal structure of AR ligand-binding domain (LBD) at antagonistic form is not available, we decided to learn from estrogen receptor (ER) antagonism: (i) We built a structural model of wild-type AR-LBD complexed with antiandrogen bicalutamide (wild type/bicalutamide) using ERα-LBD/hydroxytamoxifen structure as the template for helix-12. (ii) By comparative structural analysis of 24 ERα-LBD complexes, we found residues D351 and L354 at helix-3 adopt unique conformations, and distance between them is a marker of ERα-LBD/antagonist complexes. The AR residues corresponding to D351 and L354 are E709 and L712, respectively. We found distance between E709 and L712 of the wild type/bicalutamide model is substantially different from that of AR-LBD/agonist complexes, suggesting this distance could be a marker of antagonistic AR-LBD, which was supported by molecular dynamics simulations. Based on the wild type/bicalutamide model, we discovered compound 3 is a novel antiandrogen effective against the wild type and T877A-, W741C-, and H874Y-mutated androgen receptors. We found compound 3 has dual functions, inhibiting androgen receptor and IKK(β) .
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".