NEURAL NETWORKS AND C-13 NMR SHIFT PREDICTION

Computational neural networks are known to have the capability to pred ict complex mappings between input and output data. These new tools se em to be well-suited to NMR data. To treat simultaneously a whole set of compounds in the alkane family, we used a back-propagation neural n etwork with a topological description as input. The results allow for a good prediction of the shifts because of the range of the test popul ation (up to 62% of the known environments) and since ali types of car bons are taken into account without distinction of connectivity.