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Record W4250396770 · doi:10.21203/rs.3.rs-898551/v1

Highly Diastereoselective Cascade [5+1] Double Michael Reaction, a Route for The Synthesis of Spiro Thio-Oxindoles

2021· preprint· en· W4250396770 on OpenAlexafffund
Firouz Matloubi Moghaddam, Vahid Saberi, Ashkan Karimi

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldChemistry
TopicSynthesis of heterocyclic compounds
Canadian institutionsMcGill University
FundersIran National Science FoundationMcGill UniversitySharif University of TechnologyNational Science Foundation
KeywordsRegioselectivityChemistryThio-SelectivityCatalysisCombinatorial chemistryCascadeDocking (animal)Reaction conditionsMichael reactionOrganic chemistryChromatography

Abstract

fetched live from OpenAlex

Abstract The first diastereoselective synthesis of spiro-thiooxindole is reported via Michael reaction between thiooxindoles and divinyl ketones. The reaction was conducted without any catalyst or additive under green conditions, i.e., ethanol as the solvent and room temperature. This study showed the art of governing regioselectivity in which novel spiro frameworks were generated in high yields. In addition to simple and column chromatography-free purification, the high diastereoselectivity makes this protocol very robust. The regio- and stereo- selectivity of the reaction has been discussed in light of the theoretical calculations, and molecular docking has been performed on the reaction products. Our method presents the first synthesis of cis-spiro thiooxindoles, which can be used to generate cis-spiro oxindoles. According to our docking studies, the latter demonstrates higher binding affinities towards the LdUMPS than corresponding trans-spiro oxindoles and may be utilized to develop new hit compounds for leishmaniasis treatment.

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)0.0010.000
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 head0.068
GPT teacher head0.363
Teacher spread0.295 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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