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Record W2765104466 · doi:10.1021/jacs.7b09645

Remarkable Reactivity Differences between Glucosides with Identical Leaving Groups

2017· article· en· W2765104466 on OpenAlexafffund
Tianmeng Duo, Kyle Robinson, Ian R. Greig, Hongming Chen, Brian O. Patrick, Stephen G. Withers

Bibliographic record

VenueJournal of the American Chemical Society · 2017
Typearticle
Languageen
FieldChemistry
TopicCarbohydrate Chemistry and Synthesis
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCompute Canada
KeywordsChemistryReactivity (psychology)AglyconeLeaving groupNucleophileArylReaction rate constantStereochemistrySubstrate (aquarium)Nucleophilic substitutionHydrolysisReaction rateComputational chemistryKineticsMedicinal chemistryOrganic chemistryCatalysisGlycoside

Abstract

fetched live from OpenAlex

Two isomeric aryl 2-deoxy-2-fluoro-β-glucosides react with a β-glucosidase at rates differing by 10 6 -fold, despite the fact that they release the same aromatic aglycone. In contrast, the equivalent glucoside substrates react with essentially identical rate constants. Insight into the source of these surprising rate differences was obtained through a comprehensive study of the nonenzymatic (spontaneous) hydrolysis of these same substrates, wherein an approximate 10 5 -fold difference in rates was measured, clarifying that the differences were inherent rather than being due to specific interactions with the enzyme. The possibility that an alternate nucleophilic aryl substitution mechanism was responsible for the rapid reaction of the faster substrate was excluded through 18 O-labeling studies. Further exploration of the origins of these rate differences involved analysis of X-ray crystal structures as well as quantum chemical calculations, which surprisingly revealed that ground state destabilization and transition state stabilizing effects contribute almost equally to the observed reactivity differences. These studies highlight the dangers of using simple reference equilibria such as p K a values as measures of leaving group ability.

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 score0.098
Threshold uncertainty score0.515

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 integrity0.0000.001
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 head0.020
GPT teacher head0.254
Teacher spread0.234 · 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

Citations15
Published2017
Admission routes2
Has abstractyes

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