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Record W4313396453 · doi:10.26434/chemrxiv-2022-8pn7l

A convergent fragment coupling strategy to access quaternary stereogenic centers

2022· preprint· en· W4313396453 on OpenAlexfundno aff
Jeff Kerkovius, Alice R. Wong, Victor W. Mak, Sarah E. Reisman

Bibliographic record

VenueChemRxiv · 2022
Typepreprint
Languageen
FieldChemistry
TopicSynthetic Organic Chemistry Methods
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCalifornia Institute of TechnologyDivision of ChemistryBeckman Institute, California Institute of TechnologyNational Institutes of HealthNational Science Foundation
KeywordsStereocenterTrimethylsilyl trifluoromethanesulfonateChemistryEnolStereochemistryEnantioselective synthesisOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

The formation of quaternary stereogenic centers via convergent fragment coupling is a longstanding challenge in organic synthesis. Here, we report a strategy for the formation of quaternary stereogenic centers in polycyclic systems based upon the semi-pinacol reaction. In the key transformation, two fragments of a similar size and complexity are joined by a 1,2-addition of an alkenyl lithium to an epoxy ketone, and the resulting epoxy silyl ether undergoes a semi-pinacol rearrangement catalyzed by N-(trimethylsilyl)bis(trifluoromethanesulfonyl)imide (TMSNTf2) or trimethylsilyl trifluoromethanesulfonate (TMSOTf). Polycyclic scaffolds were generated in high yields and the reaction conditions tolerated a variety of functional groups including esters, silyl ethers, enol ethers, and aryl triflates. This method provides a useful strategy for the synthesis of complex polycyclic natural product-like scaffolds with quaternary stereogenic centers from simplified fragments.

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: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

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 science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.059
GPT teacher head0.341
Teacher spread0.282 · 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
GenreMethods

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
Published2022
Admission routes1
Has abstractyes

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