Gilberto V. Rodrigues

A computer-assisted approach for chemotaxonomic studies — diterpenes in Lamiaceae

This paper describes a new computer approach for chemotaxonomic studies. The methodology employed enables the search for chemical substructures as taxonomic descriptors using an expert system built with plant natural products. The operation of the system was tested with diterpenes as taxonomic markers in Lamiaceae.

13C NMR spectroscopy of monoterpenoids

C NMR, and their subse-quent development over the years, structure elucida-tion ofnatural products hasbecome commonpractice.As a consequence, several new isolated terpenoids,including mono-, di-, tri- and sesquiterpenes, havehad their structure established. Today studies onthousands of terpenoids have been reported but theirchemical shift data are still scattered in the literature.Much of the impetus for the study of the

Applications D'Intelligence Artificielle Dans La Chimie Organique. XVII. Nouveaux Programmes Du Projet SISTEMAT

This work describes the new improvements of the SISlEMAT project, one system for structural elucidation mainly in the field of Natural Products Chemistry. Some examples of the resolution of problems using 13C Nuclear Magnetic Resonance and Mass Spectroscopy are given. Programs to discover new heuristic rules for structure generation are discussed. The data base contains about 1000 13C NMR spectra.

Computer-aided method for identification of components in essential oils by 13C NMR spectroscopy

Abstract The aim of this paper is to present a procedure based on analyses of 13 C NMR data for identification of known and new chemical constituents in essential oils. A novel program developed to analyze complex mixtures of terpenoid compound was evaluated for the identification of components in the essential oils from leaves of Piper cernuum and Piper regnellii .

Ditregra – an auxiliary program for structural determination of diterpenes

This work describes the creation of heuristics rules based on 13 C-NMR spectroscopy that characterize several skeletal types of diterpenes. Using a collection of 2745 spectra we built a database linked to the expert system SISTEMAT. Several programs were applied to the database in order to discover characteristic signals that identify with a good performance, a large diversity of skeletal types. The heuristic approach used was able to differentiate groups of skeletons based firstly on the number of primary, secondary, tertiary and quaternary carbons, and secondly the program searches, for each group, if there are ranges of chemical shifts that identifies specific skeletal type. The program was checked with 100 new structures recently published and was able to identify the correct skeleton in 65 of the studied cases. When the skeleton has several hundreds of compounds, for example, the labdanes, the program employs the concept of subskeletal, and does not classify in the same group labdanes with double bounds at different positions. The chemical shift ranges for each subskeletal types and the structures of all skeletal types are given. The consultation program can be obtained from the authors.

Applications of artificial intelligence to structure determination of organic compounds. XX. Determination of groups attached to the skeleton of natural products using ^{13}C nuclear magnetic resonance spectroscopy

A procedure for the identification of substituent groups (viz. angelate, tiglate, etc.) attached to any of the atoms in the conventional skeleton of a natural product is described. It consists in the use of the program MACRONO, which was developed for finding subspectra due to the carbons in the said substituent groups amid the raw 13C NMR spectroscopic data from any given natural product (by means of comparisons of all possible subsets of the observed chemical shifts with those contained in an apposite database, built with literature 13C NMR spectroscopic data regarding those groups). This procedure enables one to expunge the chemical shifts not due to skeletal carbons from the initial dataset, which then can be input to the expert system SISTEMAT, for skeletal identification.

Automatic identification by 13C NMR of substituent groups bonded in natural product skeletons.

The aim of this paper is to present a procedure that utilizes 13C NMR for identification of substituent groups which are bonded to carbon skeletons of natural products. For so much was developed a new version of the program MACRONO, that presents a database with 161 substituent types found in the most varied terpenoids. This new version was widely tested in the identification of the substituents of 60 compounds that, after removal of the signals that did not belong to the carbon skeleton, served to test the prediction of skeletons by using other programs of the expert system SISTEMAT.

Automatic identification of terpenoid skeletons through 13C nuclear magnetic resonance data disfunctionalization

Abstract The proposal of this paper is to present a procedure that utilizes 13 C NMR for terpenoid skeletons identification. By this reason, a novel program named REGRAS was developed for the specialist system SISTEMAT. This program carries out an analysis of the 13 C NMR data from a given compound and, from ranges of chemical shifts, recognizes the chemical functions existing on specific positions of carbon skeletons. At the end of this procedure, the program matches the types of carbon atoms obtained against a database, displaying as analysis results the likely skeletons of the questioned substance. The program REGRAS was tested on skeleton elucidation of 35 compounds from the most varied classes of terpenoids, exhibiting excellent results in skeleton prevision precesses.

REGRAS: an auxiliary program for pattern recognition and substructure elucidation of monoterpenes ∗

The main purpose of this paper is to present a procedure that utilizes 13 C NMR for pattern recognition and substruc- ture elucidation of monoterpenes. By this reason, a novel version of the REGRAS program was developed for the specialist SISTEMAT system. This program carries out an analysis of the 13 C NMR data from a given compound and, from characteristic chemical shift ranges, recognizes the substructures and the skeleton present in a compound. At the end of this procedure, the program displays as analysis results the likely skeletons and substructures of the substance in question. The REGRAS program was tested on skeleton elucidation of 30 monoterpenes from the most varied skeleton types, exhibiting excellent results in skeleton and substructure prediction processes.

Application of artificial intelligence in organic chemistry. Part XIX. Pattern recognition and structural determination of flavonoids using

This essay describes another improvement to the expert system named SISTEMAT. The purpose of such im- provement 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 00 and 4 and 8 00 of the monomers.