Substructure Analysis as Basis for Intelligent Interpretation of Spectra

Abstract

Intelligent computer assisted interpretation of spectroscopic data should be based on the knowledge from large structure oriented data collections. Both the inspection of spectral features and the statistical evaluation of similar structures (from library searches) can provide a set of probability ranked substructures which are readily assembled to target structures. The idea of substructure analysis allows the chemist to combine the results of different interpretation strategies, different databases and different spectroscopic methods to yield the structural information desired. Thus in a multidimensional data system like SPECINFO ‘structural noise’ can be effectively suppressed, if all information available in the spectroscopic laboratory is combined in a central intelligent computer system.

Equally important is the prediction of spectral parameters in order to rank the candidates of the structure generator. Chemical shifts and coupling constants may serve as an example where substructure oriented strategies lead to an acceptable estimate of the spectral curve.