Application of recurrent neural networks in chemistry. Prediction and classification of carbon-13 NMR chemical shifts in a series of monosubstituted benzenes

The recurrent neural network is a feed-forward network ascribed to a parent neural network with feed-back connections (or in another term, oriented cycles). Its adaptation is performed by an analog of the standard back-propagation adaptation method. The recurrent neural network approach is illustrated by prediction and classification of 13C NMR chemical shifts in a series of monosubstituted benzenes. The descriptors (input activities) of functional groups are determined by 11 nonnegative integers that correspond to numbers of appearance of some substructural features in the corresponding molecular graphs. The obtained results indicate that these descriptors properly describe the basic physical and chemical nature of functional groups.