Neural Network Prediction of ~(13)C NMR Chemical Shifts of Substituted Benzenes

摘要

A multi-layer feedforward neural network was used for the prediction and assignment of ~13C NMR chemical shifts of substituted benzenes. The back-propagation neural network was trained by supervised learning with the chemical shift values of about 1000 substituted benzenes from literature. The average uncertainty for the prediction of the ~13C chemical shifts is as low as 1.1 ppm. In comparison to common incremental methods, essentially better results were obtained for highly substituted systems with interacting substituents.