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Record W3188548775 · doi:10.1021/acs.joc.1c01190

Exploring the Role of Aminocatalysis in the Dearomatization and Regioselectivity of Heteroaromatic Aldehydes

2021· article· en· W3188548775 on OpenAlexafffund
Jorge Dourado, Simarpreet Singh, Rebecca L. Davis

Bibliographic record

VenueThe Journal of Organic Chemistry · 2021
Typearticle
Languageen
FieldChemistry
TopicAsymmetric Synthesis and Catalysis
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaCompute Canada
KeywordsHeteroatomRegioselectivityIminiumStereocenterChemistryRing (chemistry)AromaticityCatalysisComputational chemistryDensity functional theoryCombinatorial chemistryOrganic chemistryEnantioselective synthesisMolecule

Abstract

fetched live from OpenAlex

Heteroaromatic aldehydes have recently received a lot of attention as a scaffold for aminocatalytic functionalization as they allow for the construction of remote stereocenters and highly complex heterocyclic compounds. In this paper, we employ computational methods (M06-2X/cc-pVTZ//M06-2X/6-31 + G(d,p) and MP2/cc-pVTZ//M06-2X/6-31 + G(d,p)) to examine the abilities of secondary amines to activate several model heteroaromatic aldehydes by promoting loss of aromaticity and formation of the reactive trienamine intermediate. The hyperhomodesmotic equations used to assess the energy penalty for dearomatization show that the formation of the iminium ion decreases the energy cost for dearomatization, especially when X = O and S. Furthermore, we also investigated the role that the catalyst and heteroatom may have on the orbital coefficients of the various positions of the trienamine intermediary in order to better understand and/or predict the regioselectivity these systems may showcase. Synergistic effects between the catalyst and the heteroatom of the aromatic ring were observed to increase electron density at the most remote positions of several of the model systems studied.

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)0.001
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 score0.011
Threshold uncertainty score0.219

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.200
Teacher spread0.180 · 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

Citations2
Published2021
Admission routes2
Has abstractyes

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