Site-Selective Functionalization of Hydroxyl Groups in Carbohydrate Derivatives
Bibliographic record
Abstract
Methods for site-selective transformations of hydroxyl groups in carbohydrate derivatives are reviewed. The construction of oligosaccharides of defined connectivity hinges on such transformations, which are also needed for the preparation of modified or non-natural sugar derivatives, the installation of naturally occurring postglycosylation modifications, the selective labeling or conjugation of carbohydrate derivatives, and the preparation of therapeutic agents or research tools for glycobiology. The review begins with a discussion of intrinsic factors and processes that can influence selectivity in reactions of unprotected or partially protected carbohydrate derivatives, followed by a description of transformations that engage two OH groups in cyclic adducts (acetals, ketals, boronic esters, and related species). An overview of the various classes of site-selective transformations of OH groups in sugars is then provided: the reactions discussed include esterification, thiocarbonylation, alkylation, glycosylation, arylation, silylation, phosphorylation, sulfonylation, sulfation, and oxidation. Emphasis is placed on recently developed methods that employ reagent or catalyst control to achieve otherwise challenging transformations or site-selectivities.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".