A concerted, rational design of type 1 17β‐hydroxysteroid dehydrogenase inhibitors: estradiol‐adenosine hybrids with high affinity
Bibliographic record
Abstract
Human estrogenic 17beta-hydroxysteroid dehydrogenase (17beta-HSD type 1) catalyzes the final step in the synthesis of active estrogens that stimulate the proliferation of breast cancer cells. Based on the initial premise to make use of the binding energies of both the substrate and cofactor sites, and molecular modeling starting from the enzyme structure, several estradiol-adenosine hybrids were designed and synthesized. Among these hybrids, EM-1745 with a linker of 8-CH2 groups is proved to be the best competitive inhibitor with a Ki of 3.0 +/- 0.8 nM. The crystal structure of the EM-1745 enzyme complex at 1.6 A provides evidence at atomic resolution of strong interactions between both the steroid and cofactor moieties and the enzyme molecule, as illustrated by a deltaA-weighted 2Fo-Fc electron density map contoured at 3.0 delta. The substrate entry loop is further stabilized in this complex compared with previous complexes of the enzyme. These results confirm our initial strategy of combining studies of structural biology and enzyme mechanism in the inhibitor design, which may be applied to other steroidogenic enzymes involved in human diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".