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Record W2941932857 · doi:10.1002/cctc.201900398

Exploiting Natural Complexity: Synthetic Terpenoid‐Alkaloids by Regioselective and Diastereoselective Hydroaminoalkylation Catalysis

2019· article· en· W2941932857 on OpenAlexafffund
Rebecca C. DiPucchio, Sorin‐Claudiu Roşca, Gayathri Athavan, Laurel L. Schafer

Bibliographic record

VenueChemCatChem · 2019
Typearticle
Languageen
FieldChemistry
TopicAsymmetric Hydrogenation and Catalysis
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaCanada Research Chairs
KeywordsEnantiopure drugAlkeneRegioselectivityChemistryStereoselectivityCatalysisStereochemistryTerpenoidTerpeneAbsolute configurationOrganic chemistryEnantioselective synthesis

Abstract

fetched live from OpenAlex

Abstract We report a catalytic and atom‐economic approach for the addition of N‐methyl groups to unactivated alkene‐containing terpenes to generate a library of synthetic terpenoid‐alkaloids. This catalysis was accomplished using Ta(CH2SiMe3)3Cl2 in combination with a new chiral ureate salt for highly chemo‐, regio‐, and diastereoselective hydroaminoalkylation reactions. The desired products are easily isolated and purified from unreacted starting materials to give aminated terpenes in one catalytic step. Starting material enantiopurity is completely retained, and stereoselective catalysis results to give enantiopure products. Absolute stereochemistry was determined by X‐ray crystallography and is consistent with regio‐ and stereoselective alkene insertion into a catalytically active tantallaziridine intermediate.

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 score0.001
Threshold uncertainty score0.003

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)0.0010.001

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 head0.007
GPT teacher head0.204
Teacher spread0.197 · 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

Citations24
Published2019
Admission routes2
Has abstractyes

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