Simulation of carbon-13 nuclear magnetic resonance spectra of quinolines and isoquinolines

Ranc, Martha L.; Jurs, Peter C.

doi:10.1016/s0003-2670(00)80883-1

Cited by 11 publications

(5 citation statements)

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“…Numerous compound classes were studied for structure-retention parameter relationships in thin-layer chromatography (121), HPLC (122)(123)(124)(125)(126)(127)(128)(129)(130), and gas chromatography (131)(132)(133)(134)(135)(136)(137)(138)(139)(140)(141)(142)(143). Structure-activity relationships in NMR spectroscopy included carbon (144,(146)(147)(148)(149)(150)(151)(152)(153)(154)(155)(156), phosphorus (145), proton (146), and nitrogen (753) shift predictions for various classes of compounds. Varmuza et al (757) discussed the use of mapping methods and exploratory data analysis in the elucidation of structure in aliphatic alcohols and alkenes from mass spectral information.…”

Section: Structure-activity Relationshipsmentioning

confidence: 99%

Chemometrics

Brown¹,

Bear²,

Blank³

1992

Anal. Chem.

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Chemometrlcs and EdHor of the CRC Chemometrics Series and he serves on the editorial boards of Analytlca Chlmlca Acta and Analytical Letters. He also serves as Acting President of the North American Chapter of the International Chemometrlcs Society, as an officer in the Chemometrlcs Society, and as a member of the Chemstatistics committee of the American Statistical Association. His research Interests encompass many aspects of the application of computers to chemical analysis. Including signal processing methods, neural networks, digital filters and modeling of interfaclal and transport processes. Robert S. Bear, Jr., earned a B.S. degree in chemistry from the University of Delaware In 1989. Currently, he Is a graduate research assistant working towards a Ph.D. In analytical chemistry at the University ol Delaware. His research Interests include Kalman filtering, electrochemical simulation, and novel ultrasound methods for process control.

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Section: Structure-activity Relationshipsmentioning

confidence: 99%

Chemometrics

Brown¹,

Bear²,

Blank³

1992

Anal. Chem.

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“…The QSPR model is developed from a data set of compounds with experimentally determined chemical shifts, and it can then be used to predict chemical shifts for carbon atoms of compounds that were not used during model development. Recently QSPR models have been developed that relate the chemical shifts of quinolines and isoquinolines, keto steroids, and tetrahydropyrans, among others, to their molecular structure.…”

Section: Introductionmentioning

confidence: 99%

Computer Assisted Simulation of ¹³C Nuclear Magnetic Spectra of Monosaccharides

and

Jurs

1996

J. Chem. Inf. Comput. Sci.

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Mathematical models are developed that relate the structures of monosaccharides to their 13 C nuclear magnetic resonance spectra. The data set of monosaccharides consists of 55 monosaccharides in the six-membered ring configuration and 56 monosaccharides in the five-membered ring configuration. The structural environment of each carbon atom in the data set is encoded using numerical atom-based descriptors which are then used to develop linear regression models relating the 13 C chemical shift to the structural features. The atom-based descriptors used in this study encode topological, geometric, and electronic information about the carbon atoms in monosaccharides. Multiple linear regression analysis is used to develop an 11-descriptor model to predict the chemical shifts of pyranoses and pyranosides and an eight-descriptor model to predict the chemical shifts of furanoses and furanosides. The models are then submitted to computational neural networks, giving improved results with final training set rms errors of 1.03 ppm for pyranoses and pyranosides and 1.58 ppm for furanoses and furanosides.

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“…As in previous work, the term "experiment" refers to computer based experiments, which, like their laboratory equivalents, are distinguished by experimental parameters. Here these are the following: (1) the 3c3v subclass used, ( 2 architectures used, (3) the different types of compound vector used, and (4) where networks are cross-evaluated, the subclass of the monitoring set used. To counteract the effects of data errors, experiments are repeated using different partitions of the unique set, which are then bundled together into experiment categories and average performance values calculated.…”

Section: Introductionmentioning

confidence: 99%

Predicting phosphorus nuclear magnetic resonance (NMR) shifts using neural networks. 3. Element-value optimizing (EVO) network architectures

West¹

1995

J. Chem. Inf. Comput. Sci.

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Simulation of carbon-13 nuclear magnetic resonance spectra of quinolines and isoquinolines

Cited by 11 publications

References 30 publications

Chemometrics

Chemometrics

Computer Assisted Simulation of ¹³C Nuclear Magnetic Spectra of Monosaccharides

Predicting phosphorus nuclear magnetic resonance (NMR) shifts using neural networks. 3. Element-value optimizing (EVO) network architectures

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Simulation of carbon-13 nuclear magnetic resonance spectra of quinolines and isoquinolines

Cited by 11 publications

References 30 publications

Chemometrics

Chemometrics

Computer Assisted Simulation of 13C Nuclear Magnetic Spectra of Monosaccharides

Predicting phosphorus nuclear magnetic resonance (NMR) shifts using neural networks. 3. Element-value optimizing (EVO) network architectures

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Product

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About

Computer Assisted Simulation of ¹³C Nuclear Magnetic Spectra of Monosaccharides