Installation of Electron-Donating Protective Groups, a Strategy for Glycosylating Unreactive Thioglycosyl Acceptors using the Preactivation-Based Glycosylation Method
Bibliographic record
Abstract
Preactivation-based chemoselective glycosylation is a powerful strategy for oligosaccharide synthesis with its successful application in assemblies of many complex oligosaccharides. However, difficulties were encountered in reactions where glycosyl donors bearing multiple electron-withdrawing groups failed to glycosylate hindered unreactive acceptors. In order to overcome this problem, it was discovered that the introduction of electron-donating protective groups onto the glycosyl donors can considerably enhance their glycosylating power, leading to productive glycosylations even with unreactive acceptors. This observation is quite general and can be extended to a wide range of glycosylation reactions, including one-pot syntheses of chondroitin and heparin trisaccharides. The structures of the reactive intermediates formed upon preactivation were determined through low-temperature NMR studies. It was found that for a donor with multiple electron-withdrawing groups, the glycosyl triflate was formed following preactivation, while the dioxalenium ion was the major intermediate with a donor bearing electron-donating protective groups. As donors were all cleanly preactivated prior to the addition of the acceptors, the observed reactivity difference between these donors was not due to selective activation encountered in the traditional armed-disarmed strategy. Rather, it was rationalized by the inherent internal energy difference between the reactive intermediates and associated oxacarbenium ion like transition states during nucleophilic attack by the acceptor.
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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 | 0.001 |
| 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".