Dataset Information

Synthesis of Silylated Cyclobutanone and Cyclobutene Derivatives Involving 1,4-Addition of Zinc-Based Silicon Nucleophiles.

ABSTRACT: A copper-catalyzed conjugate silylation of various cyclobutenone derivatives with Me₂ PhSiZnCl ⋅ 2LiCl or (Me₂ PhSi)₂ Zn ⋅ xLiCl (x≤4) to generate β-silylated cyclobutanones is reported. Trapping the intermediate enolate with ClP(O)(OPh)₂ affords silylated enol phosphates that can be further engaged in Kumada cross-coupling reactions to yield silylated cyclobutene derivatives.

SUBMITTER: Cui M

PROVIDER: S-EPMC9292915 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Publications

Synthesis of Silylated Cyclobutanone and Cyclobutene Derivatives Involving 1,4-Addition of Zinc-Based Silicon Nucleophiles.

Cui Ming M Oestreich Martin M

Chemistry (Weinheim an der Bergstrasse, Germany) 20211007 65

A copper-catalyzed conjugate silylation of various cyclobutenone derivatives with Me2 PhSiZnCl ⋅ 2LiCl or (Me2 PhSi)2 Zn ⋅ xLiCl (x≤4) to generate β-silylated cyclobutanones is reported. Trapping the intermediate enolate with ClP(O)(OPh)2 affords silylated enol phosphates that can be further engaged in Kumada cross-coupling reactions to yield silylated cyclobutene derivatives. ...[more]

PMID: 34490934

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Project description:MotivationCompound structure identification is using increasingly more sophisticated computational tools, among which machine learning tools are a recent addition that quickly gains in importance. These tools, of which the method titled Compound Structure Identification:Input Output Kernel Regression (CSI:IOKR) is an excellent example, have been used to elucidate compound structure from mass spectral (MS) data with significant accuracy, confidence and speed. They have, however, largely focused on data coming from liquid chromatography coupled to tandem mass spectrometry (LC-MS). Gas chromatography coupled to mass spectrometry (GC-MS) is an alternative which offers several advantages as compared to LC-MS, including higher data reproducibility. Of special importance is the substantial compound coverage offered by GC-MS, further expanded by derivatization procedures, such as silylation, which can improve the volatility, thermal stability and chromatographic peak shape of semi-volatile analytes. Despite these advantages and the increasing size of compound databases and MS libraries, GC-MS data have not yet been used by machine learning approaches to compound structure identification.ResultsThis study presents a successful application of the CSI:IOKR machine learning method for the identification of environmental contaminants from GC-MS spectra. We use CSI:IOKR as an alternative to exhaustive search of MS libraries, independent of instrumental platform and data processing software. We use a comprehensive dataset of GC-MS spectra of trimethylsilyl derivatives and their molecular structures, derived from a large commercially available MS library, to train a model that maps between spectra and molecular structures. We test the learned model on a different dataset of GC-MS spectra of trimethylsilyl derivatives of environmental contaminants, generated in-house and made publicly available. The results show that 37% (resp. 50%) of the tested compounds are correctly ranked among the top 10 (resp. 20) candidate compounds suggested by the model. Even though spectral comparisons with reference standards or de novo structural elucidations are neccessary to validate the predictions, machine learning provides efficient candidate prioritization and reduction of the time spent for compound annotation.

Dataset Information

Synthesis of Silylated Cyclobutanone and Cyclobutene Derivatives Involving 1,4-Addition of Zinc-Based Silicon Nucleophiles.

Publications

Synthesis of Silylated Cyclobutanone and Cyclobutene Derivatives Involving 1,4-Addition of Zinc-Based Silicon Nucleophiles.

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