Simulation of polysaccharide carbon-13 nuclear magnetic resonance spectra using regression analysis and neural networks

Ball, Jon W.; Jurs, Peter C.

doi:10.1021/ac00072a014

Cited by 22 publications

(8 citation statements)

References 17 publications

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“…Although the majority of the scientific community uses the BP algorithm, we recently reported substantial improvement in our results by using the quasi-Newton method [22]. In that work, we found that the quasi-Newton method required fewer training cycles and generated significantly more accurate I3C-NMR spectral simulations for a set of polysaccharides than the BP algorithm.…”

Section: Computational Neural Network Algorithmsmentioning

confidence: 70%

Quantitative Structure-Activity Relationships for Toxicity of Phenols Using Regression Analysis and Computational Neural Networks

Ball

Dixon

et al. 1994

Environ Toxicol Chem

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Quantitative structure-toxicity models were developed that directly link the molecular structures of a set of 50 alkylated and/or halogenated phenols with their polar narcosis toxicity, expressed as the negative logarithm of the IGCSO (50Vo growth inhibitory concentration) value in millimoles per liter. Regression analysis and fully connected, feed-forward neural networks were used to develop the models. Two neural network training algorithms (back-propagation and a quasi-Newton method) were employed. The best model was a quasi-Newton neural network that had a root-mean-square error of 0.070 log units for the 45 training set phenols and 0.069 log units for the five cross-validation set phenols.

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Section: Computational Neural Network Algorithmsmentioning

confidence: 70%

Quantitative Structure-Activity Relationships for Toxicity of Phenols Using Regression Analysis and Computational Neural Networks

Ball

Dixon

et al. 1994

Environ Toxicol Chem

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“…The most accurate results were obtained from a new quasi-Newton neural network, as was the case in previous work [22]. The performance of the network was enhanced by using the CV set to monitor the training process.…”

Section: Discussionmentioning

confidence: 88%

Quantitative structure‐activity relationships for toxicity of phenols using regression analysis and computational neural networks

Xu¹,

Ball²,

Dixon³

et al. 1994

Enviro Toxic and Chemistry

102

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Quantitative structure‐toxicity models were developed that directly link the molecular structures of a set of 50 alkylated and/or halogenated phenols with their polar narcosis toxicity, expressed as the negative logarithm of the IGC50 (50% growth inhibitory concentration) value in millimoles per liter. Regression analysis and fully connected, feed‐forward neural networks were used to develop the models. Two neural network training algorithms (back‐propagation and a quasi‐Newton method) were employed. The best model was a quasi‐Newton neural network that had a root‐mean‐square error of 0.070 log units for the 45 training set phenols and 0.069 log units for the five cross‐validation set phenols.

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“…102 Nuclear magnetic resonance (NMR) has also been a fruitful area of ANN BP application. [103][104][105][106][107][108][109][110][111] Most studies have dealt with either the simulation of 13C spectra or the prediction of 1% shifts,103-105,107-109 although one study focused on prediction of phosphorus shifts.106 A BP network was used to predict secondary protein structure, which was then used to assist in NMR assignment. 110 In another study, 1H NMR spectra of binary mixtures of alditols were successfully classified.111 BP networks were used for multivariate calibrations of pyrolysis mass spectra.112,113 In one case the BP networks gave better concentration predictions than did both partial least-squares and principal components regression methods.112 Another study with similar results also noted that linear transfer functions gave better results than sigmoid functions.113 Structural features were successfully identified in a library search of mass spectra.114 Interlaboratory calibration of two mass spectrometers has been accomplished.~l5…”

Section: Backpropagation (Bp) and Related Networkmentioning

confidence: 99%

Artificial Neural Networks and Their use in Chemistry

Peterson¹

2000

Reviews in Computational Chemistry

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INTRODUCTIONChemists are being confronted with a confounding array of data at an ever increasing pace. The advent of new experimental techniques, the development of cheaper, faster, and more precise instrumentation, and the availability of desktop computing power that only a decade ago would have filled a small house have all contributed to this situation. Medicinal chemists using combinatorial chemistry methods have generated huge libraries of chemical compounds that must be assessed for pharmaceutical activity. Spectroscopists search and analyze huge databases of spectra. Computational chemists generate vast numbers of points describing potential energy surfaces in n-dimensional spaces.The issue facing chemists today is not how to generate data, which not so long ago was actually quite difficult and time-consuming, but how to extract useful information from the data generated. As a consequence, a branch of chemistry known as chemometrics began to evolve in the late 1960s.l Chemometrics is broadly concerned with the extraction of useful information from chemical data. Until about 1990, chemometrics primarily involved appli-

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Simulation of polysaccharide carbon-13 nuclear magnetic resonance spectra using regression analysis and neural networks

Cited by 22 publications

References 17 publications

Quantitative Structure-Activity Relationships for Toxicity of Phenols Using Regression Analysis and Computational Neural Networks

Quantitative Structure-Activity Relationships for Toxicity of Phenols Using Regression Analysis and Computational Neural Networks

Quantitative structure‐activity relationships for toxicity of phenols using regression analysis and computational neural networks

Artificial Neural Networks and Their use in Chemistry

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