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13C NMR Chemical Shift Prediction of sp2 Carbon Atoms in Acyclic Alkenes Using Neural Networks

https://doi.org/10.1021/ci950131x
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/anie.199305031openalex-snapshot
Neural Networks in Chemistry
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An application of neural networks in chemistry. Prediction of13C NMR chemical shifts
inferredcites →https://doi.org/10.1016/0003-2670(94)85025-9openalex-snapshot
Integrated approach for 13C nuclear magnetic resonance shift prediction, spectral simulation and library search
inferredcites →https://doi.org/10.1016/0166-1280(92)87132-jopenalex-snapshot
Application of neural networks with feedback connections in chemistry: prediction of 13C NMR chemical shifts in a series of monosubstituted benzenes
inferredcites →https://doi.org/10.1016/0169-7439(93)80052-jopenalex-snapshot
Backpropagation neural networks
inferredcites →https://doi.org/10.1016/0169-7439(93)80105-qopenalex-snapshot
The CSEARCH-NMR data base approach to solve frequent questions concerning substituent effects on 13C NMR chemical shifts
inferredcites →https://doi.org/10.1016/0169-7439(94)00022-0openalex-snapshot
Artificial neural network simulation of 13C NMR shifts for methyl substituted cyclohexanes
inferredcites →https://doi.org/10.1016/0893-6080(89)90020-8openalex-snapshot
Multilayer feedforward networks are universal approximators
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Neural computing: Theory and practice
inferredcites →https://doi.org/10.1016/s0003-2670(00)83481-9openalex-snapshot
Comprehensive parameter set for the prediction of the 13C-NMR chemical shifts of sp3-hybridized carbon atoms in organic compounds
inferredcites →https://doi.org/10.1016/s0003-2670(00)84659-0openalex-snapshot
Parameter set for the prediction of the 13C-NMR chemical shifts of sp2- and sp-hybridized carbon atoms in organic compounds
inferredcites →https://doi.org/10.1016/s0003-2670(00)85105-3openalex-snapshot
A computer program for the prediction of 13-C-NMR chemical shifts of organic compounds
inferredcites →https://doi.org/10.1016/s0166-1280(09)80062-6openalex-snapshot
Non-linear modelling of 13C NMR chemical shift data using artificial neural networks and partial least squares method
inferredcites →https://doi.org/10.1021/ac00034a015openalex-snapshot
Prediction of carbon-13 nuclear magnetic resonance chemical shifts by artificial neural networks
inferredcites →https://doi.org/10.1021/ac00034a016openalex-snapshot
Reduction of polyatomic ion interferences in inductively coupled plasma mass spectrometry by cryogenic desolvation
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Determination of Atrazine in Water Using Tandem High-Performance Immunoaffinity Chromatography and Reversed-Phase Liquid Chromatography
inferredcites →https://doi.org/10.1021/ci00008a005openalex-snapshot
C13Shift: a computer program for the prediction of carbon-13 NMR spectra based on an open set of additivity rules
inferredcites →https://doi.org/10.1021/ci00010a023openalex-snapshot
Application of recurrent neural networks in chemistry. Prediction and classification of carbon-13 NMR chemical shifts in a series of monosubstituted benzenes
inferredcites →https://doi.org/10.1021/ci00012a012openalex-snapshot
Topological approach of carbon-13 NMR spectral simulation: Application to fuzzy substructures
inferredcites →https://doi.org/10.1021/ci00013a007openalex-snapshot
Neural networks and carbon-13 NMR shift prediction
inferredcites →https://doi.org/10.1021/ci00046a010openalex-snapshot
CSEARCH: a computer program for identification of organic compounds and fully automated assignment of carbon-13 nuclear magnetic resonance spectra
inferredcites →https://doi.org/10.1021/ci00046a011openalex-snapshot
A concise connection table based on systematic nomenclatural terms
inferredcites →https://doi.org/10.1021/ja01069a005openalex-snapshot
Nuclear Magnetic Resonance Studies of Hydrogen Bonding. II. Calculation of the Shift upon Complex Formation
inferredcites →https://doi.org/10.1021/jm00061a023openalex-snapshot
Statistics using neural networks: chance effects
inferredcites →https://doi.org/10.1021/jm00113a022openalex-snapshot
Applications of neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors
inferredcites →https://doi.org/10.1038/323533a0openalex-snapshot
Learning representations by back-propagating errors
inferredcites →https://doi.org/10.1038/323536a0openalex-snapshot
Bilateral amblyopia after a short period of reverse occlusion in kittens
inferredcites →https://doi.org/10.7551/mitpress/5236.001.0001openalex-snapshot
Parallel Distributed Processing

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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NMR Data Correlation with Chemical Structure
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Carbon‐13 Nuclear Magnetic Resonance Spectroscopy
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Artificial Neural Networks and Their use in Chemistry
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ChemInform Abstract: 13C NMR Chemical Shift Prediction of sp2 Carbon Atoms in Acyclic Alkenes Using Neural Networks
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New Neural Networks for Structure—Property Models
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Using Neural Networks for 13C NMR Chemical Shift Prediction–Comparison with Traditional Methods
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Linear and non-linear modeling for the investigation of gas chromatography retention indices of alkylbenzenes on Cit.A-4, SE-30 and Carbowax 20M
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The neural network MolNet prediction of alkane enthalpies
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The use of rule knowledgebase in predicting molecular fragments from 13C NMR spectra — pros and cons
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CAST/CNMR: highly accurate 13C NMR chemical shift prediction system considering stereochemistry
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Use of artificial neural networks to predict the gas chromatographic retention index data of alkylbenzenes on carbowax-20M
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Automated Structure Elucidation of Organic Molecules from 13C NMR Spectra Using Genetic Algorithms and Neural Networks
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13C NMR Chemical Shift Prediction of the sp3 Carbon Atoms in the α Position Relative to the Double Bond in Acyclic Alkenes
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Estimation of the Liquid Viscosity of Organic Compounds with a Quantitative Structure−Property Model
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A General13C NMR Spectrum Predictor Using Data Mining Techniques
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Predicting the Standard Enthalpy (ΔHof) and Entropy (So) of Alkanes by Artificial Neural Networks
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Can Graph Machines Accurately Estimate 13C NMR Chemical Shifts of Benzenic Compounds?
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