Rational discovery of novel nuclear hormone receptor antagonists
Bibliographic record
Abstract
Nuclear hormone receptors (NRs) are potential targets for therapeutic approaches to many clinical conditions, including cancer, diabetes, and neurological diseases. The crystal structure of the ligand binding domain of agonist-bound NRs enables the design of compounds with agonist activity. However, with the exception of the human estrogen receptor-alpha, the lack of antagonist-bound "inactive" receptor structures hinders the rational design of receptor antagonists. In this study, we present a strategy for designing such antagonists. We constructed a model of the inactive conformation of human retinoic acid receptor-alpha by using information derived from antagonist-bound estrogen receptor-alpha and applied a computer-based virtual screening algorithm to identify retinoic acid receptor antagonists. Thus, the currently available crystal structures of NRs may be used for the rational design of antagonists, which could lead to the development of novel drugs for a variety of diseases.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".