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Prediction of 1H NMR Coupling Constants with Associative Neural Networks Trained for Chemical Shifts

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REQUIMTE and CQFB, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
Cite this: J. Chem. Inf. Model. 2007, 47, 6, 2089–2097
Publication Date (Web):October 23, 2007
https://doi.org/10.1021/ci700172n
Copyright © 2007 American Chemical Society
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    Abstract

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    Fast accurate predictions of 1H NMR spectra of organic compounds play an important role in structure validation, automatic structure elucidation, or calibration of chemometric methods. The SPINUS program is a feed-forward neural network (FFNN) system developed over the last 8 years for the prediction of 1H NMR properties from the molecular structure. It was trained using a series of empirical proton descriptors. Ensembles of FFNNs were incorporated into Associative Neural Networks (ASNN), which correct a prediction on the basis of the observed errors for the k nearest neighbors in an additional memory. Here we show a procedure to estimate coupling constants with the ASNNs trained for chemical shiftsa second memory is linked consisting of coupled protons and their experimental coupling constants. An ASNN finds the pairs of coupled protons most similar to a query, and these are used to estimate coupling constants. Using a diverse general data set of 618 coupling constants, mean absolute errors of 0.6−0.8 Hz could be achieved in different experiments. A Web interface for 1H NMR full-spectrum prediction is available at http://www.dq.fct.unl.pt/spinus.

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    Initial pool of proton descriptors, selected descriptors used as input to the final neural networks, and spectroscopic data for compound 1. This material is available free of charge via the Internet at http://pubs.acs.org.

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