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Record W2163324680 · doi:10.1002/jlcr.552

Proton and metal‐ion activation of C–H exchange in five‐membered azoles

2002· article· en· W2163324680 on OpenAlexafffund
Erwin Buncel, Ikenna Onyido

Bibliographic record

VenueJournal of Labelled Compounds and Radiopharmaceuticals · 2002
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicChemical Reactions and Isotopes
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryCatalysisProtonationMetalDissociation (chemistry)Ligand (biochemistry)Hydrogen bondMedicinal chemistryHydrideHeterolysisStereochemistryInorganic chemistryIonOrganic chemistryMolecule

Abstract

fetched live from OpenAlex

Abstract Factors influencing C–H isotopic exchange rates in five‐membered azoles, that is imidazoles and thiazoles, under catalysis by H + and M n + , especially transition metals, Pt(II) and Co(III) are discussed. Hydrogen ion catalysis through N(3) protonation of azoles 1–3 is generally the most efficient, with rate enhancements in the range 10 2 –10 9 over the neutral process being attained. Metal‐ion coordination also results in effective catalysis, though less so than catalysis by protons. Catalysis of C–H exchange by M n + can be studied through addition of the metal salts to a buffered solution of the heterocycle in which labile complexes exist, or on synthesized complexes such as 4–13 which are substitution‐inert thus precluding complications from unknown dissociation equilibria. A delicate balance of factors influence the ease of C–H exchange, including: (1) the magnitude of the fractional charge located at N(3) of the heterocycle through M n + –N(3) σ bond polarization; (2) metal‐to‐ligand π back‐bonding; (3) the electronic structure of the metal ions. These considerations have obvious consequences for deuterium‐ and tritium‐labelling of a number of biomolecules, e.g. proteins, enzymes, nucleic acids, some vitamins, as well as drugs which incorporate five‐membered azoles in their structures. Copyright © 2002 John Wiley & Sons, Ltd.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.293
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.122
GPT teacher head0.410
Teacher spread0.288 · 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

Citations12
Published2002
Admission routes2
Has abstractyes

Explore more

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