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Record W2893885850 · doi:10.1002/ejoc.201801053

A Computational Determination of the Origins of Diastereoselective Alkylations of a Cysteinesulfenate Anion

2018· article· en· W2893885850 on OpenAlexafffund
Adrian L. Schwan

Bibliographic record

VenueEuropean Journal of Organic Chemistry · 2018
Typearticle
Languageen
FieldChemistry
TopicSulfur-Based Synthesis Techniques
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Guelph
KeywordsChemistryStereoselectivityAlkylationLithium (medication)Density functional theoryHydrogen bondFunctional groupSulfenic acidCounterionMedicinal chemistryIonSteric effectsHydrogenComputational chemistryStereochemistryOrganic chemistryMoleculeCatalysisEnzyme

Abstract

fetched live from OpenAlex

Sulfenic acid anions (RSO–) represent an untapped functional group for the formation of sulfoxides and other organic compounds. Their stereoselective alkylation is an important component of this chemistry, but factors governing reaction outcomes are not fully understood. The current study uses Density Functional Theory methods to break down the influencing roles of substituents attached to 2‐aminoethanesulfenate. The lithium counterion can be coordinated to pendant ester or carbamate carbonyl groups, whereas the sulfenate oxygen readily participates in hydrogen bonding with proximal hydrogen atoms of the (protected) amino group. A Moc‐protected, ester substituted, 2‐aminoethanesulfenate adopts both lithium coordination and hydrogen bonding in the lowest energy form and demonstrates stereoselective methylation and benzylation consistent with experiments from the literature.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.011
GPT teacher head0.232
Teacher spread0.222 · 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 designSimulation or modeling
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

Citations6
Published2018
Admission routes2
Has abstractyes

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Same venueEuropean Journal of Organic ChemistrySame topicSulfur-Based Synthesis TechniquesFrench-language works237,207