NEURAL-NETWORK PREDICTION OF C-13 NMR CHEMICAL-SHIFTS OF ALKANES

Three-layer feed-forward neural networks for the prediction of C-13 NM R chemical shifts of alkanes through nine carbon atoms are used. Carbo n atoms in alkanes are determined by 13 descriptors that correspond to the so-called embedding frequencies of rooted subtrees. These descrip tors are equal to numbers of appearance of smaller structural skeleton s composed of two through five carbon atoms. It is demonstrated that t he used descriptors offer a very useful formal tool for the proper and adequate description of environment of carbon atoms in alkanes. Neura l networks with different numbers of hidden neurons have been examined . Best results are given by the neural network composed of three hidde n neurons. Simultaneous calculations carried out by the standard linea r regression analysis are compared with our neural network calculation s.