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Record W2514057855 · doi:10.1139/cjc-2016-0307

Guanidine-catalyzed asymmetric Strecker reaction: modes of activation and origin of stereoselectivity

2016· article· en· W2514057855 on OpenAlexvenueno aff
Hansong Xue, Choon‐Hong Tan, Ming Wah Wong

Bibliographic record

VenueCanadian Journal of Chemistry · 2016
Typearticle
Languageen
FieldChemistry
TopicAsymmetric Synthesis and Catalysis
Canadian institutionsnot available
FundersNational University of Singapore
KeywordsChemistryBifunctionalTransition stateImineNon-covalent interactionsCatalysisArylGuanidineLewis acids and basesHydrogen bondStrecker amino acid synthesisBrønsted–Lowry acid–base theoryMedicinal chemistryStereochemistryComputational chemistryEnantioselective synthesisOrganic chemistryMolecule

Abstract

fetched live from OpenAlex

Density functional theory calculations were employed to study the catalytic mechanism, modes of activation, and origin of enantioselectivity of guanidine-catalyzed asymmetric Strecker reaction of N-benzhydryl imine with hydrogen cyanide. Two types of bifunctional activation mode were identified, namely conventional bifunctional Brønsted acid activation and unconventional bifunctional Brønsted–Lewis acid activation. The lowest-energy transition states correspond to the conventional bifunctional mode of activation. The calculated enantiomeric excess, based on eight lowest-energy C–C bond forming transition states, is in good accord with observed enantioselectivity. NCI (noncovalent interaction) analysis of the key transition states reveals extensive noncovalent interactions, including aromatic interactions and hydrogen bonds, between the guanidinium catalyst and substrates. Multiple aryl–aryl interactions between the phenyl groups of guanidine catalyst and the phenyl rings of N-benzhydryl imine are the key stabilizations in the most stable (R)-inducing transition state. Differential attractive aryl–aryl stabilization is the major factor for stereoinduction.

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 score0.070
Threshold uncertainty score0.424

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 integrity0.0000.000
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 head0.015
GPT teacher head0.217
Teacher spread0.202 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

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