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Record W2766227705 · doi:10.1021/jacs.7b09420

Site-Selective, Copper-Mediated <i>O</i>-Arylation of Carbohydrate Derivatives

2017· article· en· W2766227705 on OpenAlexafffund
Victoria Dimakos, Graham E. Garrett, Mark S. Taylor

Bibliographic record

VenueJournal of the American Chemical Society · 2017
Typearticle
Languageen
FieldChemistry
TopicCarbohydrate Chemistry and Synthesis
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and ScienceUniversity of TorontoCanada Foundation for InnovationMcLean Foundation
KeywordsChemistryGlycobiologyArylReactivity (psychology)Combinatorial chemistryCarbohydrate chemistryCarbohydrateSugarOrganic chemistryCarbohydrate synthesisBiochemistryGlycanGlycoprotein

Abstract

fetched live from OpenAlex

Site-selective functionalization of hydroxy groups in sugar derivatives is a major challenge in carbohydrate synthesis. Methods for achieving this goal will provide efficient access to new sugar-derived chemical building blocks and will facilitate the preparation or late-stage modification of complex oligosaccharides for applications in glycobiology research and drug discovery. Here, we describe site-selective, copper-promoted couplings of boronic acids with carbohydrate derivatives. These reactions generate sugar-derived aryl ethers, a structural class that is challenging to generate by other means and has not previously been accessed in a site-selective fashion. Experimental evidence and computational modeling suggest that the formation of a sugar-derived boronic ester intermediate is crucial to the selectivity of these processes, accelerating the arylation of an adjacent hydroxy group. The results demonstrate how the interactions of sugars with boron compounds can be combined with transition metal catalysis to achieve new chemical reactivity.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.247
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations54
Published2017
Admission routes2
Has abstractyes

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