MétaCan
Menu
Back to cohort
Record W2887765429 · doi:10.1038/s42004-018-0042-y

Catalytic N-modification of α-amino acids and small peptides with phenol under bio-compatible conditions

2018· article· en· W2887765429 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCommunications Chemistry · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChemical Synthesis and Analysis
Canadian institutionsMcGill University
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesNatural Sciences and Engineering Research Council of CanadaConsejo Nacional de Ciencia y TecnologíaMcGill University
KeywordsChemistryPhenolAmino acidOrganic chemistryBiomoleculeRacemizationCatalysisSurface modificationCombinatorial chemistryReagentBiochemistry

Abstract

fetched live from OpenAlex

Abstract The functionalization of α-amino acids and peptides provides the opportunity to tailor the properties of these biomolecules for diverse applications in chemistry and biology. Previous methods for N -modification involve the use of aliphatic alcohols, aldehydes, or halides. Alternatively, phenolic compounds are more desirable alkylating reagents as they constitute the backbone of lignin, making them an attractive bio-renewable resource. Here we report a method to N -modify 17 out of the 20 amino acids with phenol or derivatives, with water as the sole by-product. N -arylation is achieved using 2-cyclohexen-1-one and cyclohexanone as the coupling partners. Notably, phenol is successfully used to N -cyclohexylate α-amino acids and small peptides in excellent yields under bio-compatible conditions without racemization.

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.

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)0.000
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 score0.007
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.027
GPT teacher head0.275
Teacher spread0.248 · 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