Preparation of 16β-Estradiol Derivative Libraries as Bisubstrate Inhibitors of 17β-Hydroxysteroid Dehydrogenase Type 1 Using the Multidetachable Sulfamate Linker
Bibliographic record
Abstract
Combinatorial chemistry is a powerful tool used to rapidly generate a large number of potentially biologically active compounds. In our goal to develop bisubstrate inhibitors of 17beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD1) that interact with both the substrate (estrone or estradiol) and the cofactor (NAD(P)H) binding sites, we used parallel solid-phase synthesis to prepare three libraries of 16beta-estradiol derivatives with two or three levels of molecular diversity. From estrone, we first synthesized a sulfamate precursor that we loaded on trityl chloride resin using the efficient multidetachable sulfamate linker strategy recently developed in our laboratory. We then introduced molecular diversity [one or two amino acid(s) followed by a carboxylic acid] on steroid nucleus by Fmoc peptide chemistry. Finally, after a nucleophilic cleavage, libraries of 30, 63 and 25 estradiol derivatives were provided. A library of 30 sulfamoylated estradiol derivatives was also generated by acidic cleavage and its members were screened for inhibition of steroid sulfatase. Biological evaluation on homogenated HEK-293 cells overexpressing 17beta-HSD1 of the estradiol derivatives carrying different oligoamide-type chains at C-16 first revealed that three levels of molecular diversity (a spacer of two amino acids) were necessary to interact with the adenosine part of the cofactor binding site. Second, the best inhibition was obtained when hydrophobic residues (phenylalanine) were used as building blocks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".