Homoserine‐derived cyclic sulfamidate as chiral educt for the diversity‐oriented synthesis of lactam‐bridged dipeptides
Bibliographic record
Abstract
Introduction of structural constraint into peptides is an effective way for studying their conformation-activity relationships. Conformationally restrained dipeptidyl lactams, important building blocks for the synthesis of peptidomimetics, have now been synthesized from N-[9-(9-phenylfluorenyl)]-L-aspartic acid alpha-cumyl beta-methyl diester as an inexpensive chiral educt. After selective reduction of the beta-methyl ester with diisobutylaluminum hydride (DIBAL-H), homoserine was treated with thionyl chloride, imidazole, and triethylamine to give sulfamidites. Diastereoisomers were separated by chromatography and oxidation of the major sulfamidite (2R,4S)- with catalytic ruthenium trichloride afforded sulfamidate. A series of gamma-lactam-bridged dipeptides was then obtained by ring opening of sulfamidate cumyl ester with a series of amino esters, selective cumyl ester removal, and lactam formation. The resulting dipeptidyl lactams possessed aliphatic, aromatic, amino, thioether, and carboxylate side chains. A gamma-lactam analog of Pro-Leu-Gly-NH2 (PLG), was synthesized to illustrate the potential for using this approach in the synthesis of biologically active peptide mimics.
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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) | 0.000 | 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) | 0.001 | 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".