TY - JOUR
T1 - Prediction of H-1 NMR coupling constants with associative neural networks trained for chemical shifts
AU - Marques, Maria Manuel Martinho Sequeira Barata
AU - Sousa, João Montargil Aires de
PY - 2007/1/1
Y1 - 2007/1/1
N2 - Fast accurate predictions of H-1 NMR spectra of organic compounds play an important role in structure validation, automatic structure elucidation, or calibration of chemometrie methods. The SPINUS program is a feed-forward neural network (FFNN) system developed over the last 8 years for the prediction of H-1 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 shifts-a 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 predichon is available at http://www.dq.fct.unl.pt/spinus.
AB - Fast accurate predictions of H-1 NMR spectra of organic compounds play an important role in structure validation, automatic structure elucidation, or calibration of chemometrie methods. The SPINUS program is a feed-forward neural network (FFNN) system developed over the last 8 years for the prediction of H-1 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 shifts-a 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 predichon is available at http://www.dq.fct.unl.pt/spinus.
U2 - 10.1021/ci700172n
DO - 10.1021/ci700172n
M3 - Article
VL - 47
SP - 2089
EP - 2097
JO - Journal of Chemical Information and Modeling
JF - Journal of Chemical Information and Modeling
SN - 1549-9596
IS - 6
ER -