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This essay describes another improvement to the expert system named SISTEMAT. The purpose of such improvement is to help chemists who work with natural products to figure out chemical structures. SISTEMAT uses Nuclear Magnetic Resonance (NMR) 13 C data to ensemble compatible substructures according to related spectra. The system also is able to suggest a list of probable carbon skeletons. Those will work as models to structure generating programs, reducing the combinatorial explosion problem. This is the first essay from our research group which shows our system applications to aromatic compounds. A database with 700 NMR 13 C spectra of flavonoids obtained from the literature was used. We applied heuristic SISTEMAT in order to discover ranges of chemical shifts that characterise several skeleton types. The diversity of flavonoid structures is due to several oxidation patterns at rings A and B. This phenomenon causes a great complexity in the absorptions at the aromatic region. Heuristic SISTEMAT was able to discover more accurate rules that differentiate specific patterns of oxidation for some skeleton types. The performance of the software was checked against a higher complex structure of a dimeric flavonoid recently isolated. The system gives only two possibilities of skeleton types (among 70 others). Both substructures found by the program showed correct linkages between carbons 2 and 7 ″ and 4 and 8 ″ of the monomers.

spectroscopy

Application of artificial intelligence in organic chemistry. Part XIX<sup>*</sup>. Pattern recognition and structural determination of flavonoids using<sup>13</sup>C‒NMR spectra

Application of artificial intelligence in organic chemistry. Part XIX*. Pattern recognition and structural determination of flavonoids using13C‒NMR spectra

Application of artificial intelligence in organic chemistry. Part XIX^*. Pattern recognition and structural determination of flavonoids using¹³C‒NMR spectra