The Impact of Available Experimental Data on the Prediction of ¹H NMR Chemical Shifts by Neural Networks

Two different ways were explored to incorporate new available experimental data into previously trained ensembles of feed-forward neural networks, for the structure-based prediction of (1)H NMR chemical shifts of organic compounds. One approach used the new data as the memory of an associative neural network (ASNN) system. For an independent prediction set of 952 cases, a mean average error of 0.19 ppm was achieved (0.13 ppm for 90% of the cases). This approach advantageously avoids retraining the networks, and the predictions compared favorably with those obtained by available commercial software packages. Excellent predictions could also be achieved by retraining the networks with the new data, but only if the training sets were selected so as to be balanced or if the retraining started with the weights of the previously trained networks.