Discovery of diverse thyroid hormone receptor antagonists by high-throughput docking
Bibliographic record
Abstract
Treatment of hyperthyroidism, a common clinical condition that can have serious manifestations in the elderly, has remained essentially unchanged for >30 years. Directly antagonizing the effect of the thyroid hormone at the receptor level may be a significant improvement for the treatment of hyperthyroid patients. We built a computer model of the thyroid hormone receptor (TR) ligand-binding domain in its predicted antagonist-bound conformation and used a virtual screening algorithm to select 100 TR antagonist candidates out of a library of >250,000 compounds. We were able to obtain 75 of the compounds selected in silico and studied their ability to act as antagonists by using cultured cells that express TR. Fourteen of these compounds were found to antagonize the effect of T3 on TR with IC50s ranging from 1.5 to 30 microM. A small virtual library of compounds, derived from the highest affinity antagonist (1-850) that could be rapidly synthesized, was generated. A second round of virtual screening identified new compounds with predicted increased antagonist activity. These second generation compounds were synthesized, and their ability to act as TR antagonists was confirmed by transfection and receptor binding experiments. The extreme structural diversity of the antagonist compounds shows how receptor-based virtual screening can identify diverse chemistries that comply with the structural rules of TR antagonism.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".