Structure-based predictions of 1H NMR chemical shifts of sesquiterpene lactones using neural networks

In this work the prediction of 1H NMR chemical shifts of CHn protons of sesquiterpene lactones by means of neural networks is described. This method is based on the incorporation of experimental chemical shifts of protons of sesquiterpene lactones as additional memory of an associative neural network system previously trained with chemical shifts of other organic compounds. One advantage of this method is its ability to distinguish between CH2 diastereotopic protons belonging to rigid substructures since stereochemistry is considered. This is achieved via the automatic conversion of the 2D structure diagram into a 3D molecular structure. The predicted 1H NMR chemical shifts of the sesquiterpene lactones showed a high level of accuracy. This is the first report on a fully automatic proton assignment of structures of sesquiterpene lactones of an accuracy that allows its use in structure elucidation.