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Record W4400349334 · doi:10.1021/jacs.4c03685

Enantioselective Carbon Isotope Exchange

2024· article· en· W4400349334 on OpenAlexafffund
Michael G. J. Doyle, Odey Bsharat, Anna Sib, Volker Derdau, Rylan J. Lundgren

Bibliographic record

VenueJournal of the American Chemical Society · 2024
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicChemical Reactions and Isotopes
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaMitacsKillam TrustsCanada Foundation for Innovation
KeywordsChemistryEnantioselective synthesisIsotopes of carbonCarbon fibersOrganic chemistryCatalysisTotal organic carbon

Abstract

fetched live from OpenAlex

The synthesis of isotopically labeled organic molecules is vital for drug and agrochemical discovery and development. Carbon isotope exchange is emerging as a leading method to generate carbon-labeled targets, which are sought over hydrogen-based labels due to their enhanced stability in biological systems. While many bioactive small molecules bear carbon-containing stereocenters, direct enantioselective carbon isotope exchange reactions have not been established. We describe the first example of an enantioselective carbon isotope exchange reaction, where (radio)labeled α-amino acids can be generated from their unlabeled precursors using a stoichiometric chiral aldehyde receptor with isotopically labeled CO 2 followed by imine hydrolysis. Many proteinogenic and non-natural derivatives undergo enantioselective labeling, including the late-stage radiolabeling of complex drug targets.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.049
GPT teacher head0.398
Teacher spread0.349 · 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 source (direct Gemma or distilled Codex), 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

Citations21
Published2024
Admission routes2
Has abstractyes

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