Solid-Phase Synthesis of Hydroxysteroid Derivatives Using the Diethylsilyloxy Linker
Bibliographic record
Abstract
Four different types of hydroxysteroids (primary alcohol, secondary alcohols, and phenol), bearing either an oxirane or an azide as a precursor of molecular diversity, were linked in good yields to solid support using the butyldiethylsilane polystyrene (PS-DES) resin. These molecules were then used as scaffolds to generate hydroxysteroid derivatives containing two levels of diversity. The proposed libraries were tested by running steroidal alcohols through a model sequence of reactions (solid-phase coupling, aminolysis of oxirane or reduction of azide, amidation, and final cleavage). As a result, two linked secondary alcohols (17beta-hydroxy-spiro-3(R)-oxirane-5alpha-androstane and 3beta-hydroxy-spiro- 17(S)-oxirane-5alpha-androstane) and a primary alcohol (spiro-17(S)-oxirane-3-(hydroxymethyl)-1,3,5(10)-estratriene) afforded good overall yields (>45%) and high HPLC purities (>90%) of hydroxysteroids derivatized as alkylamides without purification. One limitation was noted for the fourth library: the phenolic steroid linked by the diethylsilyloxy linker gave a poor overall yield of 8% of the desired model compound. Finally, the diethylsilyloxy linker was used successfully for a rapid solid-phase synthesis of a model library of twenty C19-steroid derivatives (3beta-amido-3alpha-hydroxy-5alpha-androstane-17-ones), with an average yield of 53% and average HPLC purity of 97% without purification steps.
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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) | 0.000 | 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) | 0.002 | 0.002 |
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".