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Record W2557004559 · doi:10.1002/poc.3403

Designing kinetically stable aryltrifluoroborates as <sup>18</sup>F‐capture agents for PET imaging

2015· article· en· W2557004559 on OpenAlexafffund
Zhibo Liu, Ying Li, Richard Ting, David M. Perrin

Bibliographic record

VenueJournal of Physical Organic Chemistry · 2015
Typearticle
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Cancer Society Research InstituteCanadian Institutes of Health Research
KeywordsChemistryBiomoleculeNucleophileAqueous solutionRadiosynthesisCombinatorial chemistryFluorideIonComputational chemistryOrganic chemistryInorganic chemistry

Abstract

fetched live from OpenAlex

One‐step 18 F‐radiolabeling of peptides and other large biomolecules has been challenged by a critical gap in chemical compatibility between fluoride anion, which is unreactive as a nucleophile in water, and large biomolecules such as peptides that are insoluble in dry solvents. Traditionally, this disparity has been overcome through the preliminary synthesis of an 18 F‐labeled radioprosthetic group that is appended to peptides following at least one additional step. Ideally, however, peptides should be labeled in a single aqueous step. Hence, we proposed the use of arylboronic acids as captors of aqueous 18 F‐fluoride ion to simplify the radiosynthesis of 18 F‐aryltrifluoroborate bioconjugates. Yet, the use of aryltrifluoroborates as radioprosthetic groups can only be considered if kinetically stable ones can be designed. Herein, we discuss the kinetic and thermodynamic parameters review our previous use of a Hammett analysis that can inform the design of kinetically stable 18 F‐labeled aryltrifluoroborates for use as novel radioprosthetic groups. Copyright © 2014 John Wiley &amp; 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)0.000
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 score0.017
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.032
GPT teacher head0.327
Teacher spread0.296 · 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

Citations7
Published2015
Admission routes2
Has abstractyes

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