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Record W4387572289 · doi:10.26434/chemrxiv-2023-rqhmz

Enantiospecific Cross-coupling of Cyclic Alkyl Sulfones

2023· preprint· en· W4387572289 on OpenAlexafffund
Zachary T. Ariki, Roberto Nolla‐Saltiel, Stefanie Schiele, Jana Alpin, Yasuyo Tahara, Masakazu Nambo, Cathleen M. Crudden

Bibliographic record

VenueChemRxiv · 2023
Typepreprint
Languageen
FieldChemistry
TopicSulfur-Based Synthesis Techniques
Canadian institutionsQueen's University
FundersJapan Society for the Promotion of ScienceNatural Sciences and Engineering Research Council of CanadaQueen's UniversityBaden-Württemberg Stiftung
KeywordsElectrophileDeprotonationAlkylEnantioselective synthesisChirality (physics)ChemistryMoleculeCoupling (piping)ReagentComputational chemistryCombinatorial chemistryChemical physicsOrganic chemistryCatalysisMaterials sciencePhysicsSymmetry breaking

Abstract

fetched live from OpenAlex

Methods to form carbon-carbon bonds efficiently and with control of stereochemistry are critical for the construction of complex molecules. Cross-coupling reactions are among the most efficient and widely used reactions to construct molecules, with reactions enabling the retention or installation of chirality as recent additions to this powerful toolbox. Sulfones are robust, accessible organic electrophiles that have many attractive features as cross-coupling partners, however since the first example of their use in 1979, there have been no examples of their use in enantio-selective, -specific or -convergent cross-couplings. The high acidity of sulfones makes it unclear whether this transformation is even possible outside tertiary systems. Herein we report the enantiospecific cross-coupling of cyclic sulfones and Grignard reagents. Up to 99% chirality transfer is observed despite the strong basicity of the Grignard components. In situ monitoring reveals that the cross-coupling is kinetically competitive with competing deprotonation, resulting in a highly enantioselective transformation.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.319
Teacher spread0.261 · 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.

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

Citations0
Published2023
Admission routes2
Has abstractyes

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