Tools Prediction of NMR Chemical Shifts

Fast and accurate predictions of H NMR chemical shifts of organic compounds arc of great intcrc.st for automatic stnicturc elucidation, for the analysi.s of combinatorial libraries, and, of course, for assisting experimental chemists in the structural characterization of small data sets of compounds. 

In this section, a fast neural network approach [46] for the predication of NMR chemical shifts is explained and exemplified. 

A combination of physicochemical, topological, and geometric information is used to encode the environment of a proton, The geometric information is based on (local) proton radial distribution function (RDF) descriptors and characterizes the 3D environment of the proton. Counterpropagation neural networks established the relationship between protons and their h NMR chemical shifts (for details of neural networks, see Section 9,5). Four different types of protons were 

An extended set of physicochemical descriptors was used in this study, including, for example, partial atomic charge and effective polari2 ability of the protons, average of electronegativities of atoms two bonds away, or maximum, T-atomic charge of atoms two bonds away. 

Geometric descriptors were based on local RDF descriptors (see Eq, (16 ) for the proton j. 

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