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

Abstract

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 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 prediction is available at http://www.dq.fct.unl.pt/spinus.

Polycondensation Resins by Flavonoid Tannins Reaction with Amines

“…The 13 C NMR spectra were simulated with ACD/I-Lab version 12.0 (Advanced Chemistry Development Inc., Strasbourg, France) and with a free program online nmrdb.org [15] (https://www.nmrdb.org/).…”

“…Moreover, the disappearing of the peak at 68 ppm observed on the pTSA-catalysed 100 °C spectrum and absent The simulation of the spectra with ACD/I-Lab yields a value of 41.8 ppm for the reacted amine, and 38.7 ppm for the unreacted amine. The online software at nmrdb.org [15] yields 44.0 ppm for the covalently reacted amine formed and 41.9 ppm for the nonreacted amine. The superposition of the pTSA-catalysed spectra at 185 and 100 • C shown in Figure 20 indicates that the formation of the amine is indeed occurring.…”

Fast and accurate algorithm for the simulation of NMR spectra of large spin systems

“…This approach relies on the accuracy of the prediction of the NMR parameters and on the accuracy of the simulation of the spectrum. Several algorithms available for the prediction of chemical shifts and scalar coupling constants are fast and reliable [5][6][7][8][9][10][11][12][13]. Unfortunately, the simulation of the spectrum using spin dynamics scales exponentially with the number of atoms, and thereby making the available algorithms unsuitable for large systems [14][15][16][17][18][19][20][21][22][23][24].…”

Spin System Modeling of Nuclear Magnetic Resonance Spectra for Applications in Metabolomics and Small Molecule Screening

“…The GISSMO software package provides multiple approaches for creating the initial spin system matrix (Supporting Information). Here, the initial spin system matrices were generated by executing the NMRdb 33 (MestreNova v10.0.1, http://mestrelab.com/) and Gaussian (http://www.gaussian.com/) software packages on the 3D structure files. The functions of the graphical user interface were used to optimize the initial spin system matrices against the experimental NMR spectra.…”