The Prediction of Carbon-13 NMR Chemical Shifts Using Ensembles of Networks

| Ensembles of multi-layer network is set up to predict the carbon-13 nuclear magnetic resonance (C13 NMR) chemical shifts of a series of mono-substituted benzenes. The descriptors (inputs) used are twelve structural-based vectors that correspond to the calculated H uckel and Gasteiger electron densities of the mono-substituted aromatic systems and four graphical descriptors that correspond to the numbers of appearance of some speciic structural features of the substitutents. The outputs are the C13 NMR chemical shifts of the ipso, ortho, meta, and para carbons. A training set of 38 data was used and, after training, the neural network was tested for its ability to predict the C13 NMR chemical shifts of 15 compounds not included in the training set. In this paper, we demonstrated that the performance of artiicial neural networks in C13 NMR chemical shift prediction could be improved by (a) using both structural-based and graphical descriptors as input parameters. (b) pruning. (c) combining the prediction from a number of networks. Furthermore, pruning the connection weights can also enable us to select the appropriate input variables.