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Record W4200339904 · doi:10.1002/ange.202106996

Iron‐Catalyzed Reductive Cyclization by Hydromagnesiation: A Modular Strategy Towards <i>N</i>‐Heterocycles

2021· article· en· W4200339904 on OpenAlexafffund
Joachim Loup, Egor M. Larin, Mark Lautens

Bibliographic record

VenueAngewandte Chemie · 2021
Typearticle
Languageen
FieldChemistry
TopicCatalytic C–H Functionalization Methods
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsElectrophileChemistryCatalysisCombinatorial chemistryIntramolecular forceHydrideReactivity (psychology)NucleophileReductive eliminationOrganic chemistryMetal

Abstract

fetched live from OpenAlex

Abstract A reductive cyclization to prepare a variety of N‐heterocycles, through the use of ortho‐vinylanilides, is reported. The reaction is catalyzed by an inexpensive and bench‐stable iron complex and generally occurs at ambient temperature. The transformation likely proceeds through hydromagnesiation of the vinyl group, and trapping of the in situ generated benzylic anion by an intramolecular electrophile to form the heterocycle. This iron‐catalyzed strategy was shown to be broadly applicable and was utilized in the synthesis of substituted indoles, oxindoles and tetrahydrobenzoazepinoindolone derivatives. Mechanistic studies indicated that the reversibility of the hydride transfer step depends on the reactivity of the tethered electrophile. The synthetic utility of our approach was further demonstrated by the formal synthesis of a reported bioactive compound and a family of natural products.

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)0.000
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.001

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 head0.268
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

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

Citations2
Published2021
Admission routes2
Has abstractyes

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Same venueAngewandte ChemieSame topicCatalytic C–H Functionalization MethodsFrench-language works237,207