Sandra A. V. Alvarenga

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The training and the application of a neural network system for the prediction of occurrences of secondary metabolites belonging to diverse chemical classes in the Asteraceae is described. From a database containing about 604 genera and 28,000 occurrences of secondary metabolites in the plant family, information was collected encompassing nine chemical(More)
Feed-forward neural networks (FFNNs) were used to predict the skeletal type of molecules belonging to six classes of terpenoids. A database that contains the 13C NMR spectra of about 5000 compounds was used to train the FFNNs. An efficient representation of the spectra was designed and the constitution of the best FFNN input vector format resorted from an(More)
Motivation. Kohonen Self–Organizing Feature Map (SOM Kohonen map) is a technique used for pattern classification. The method can be applied to classify different classes of organic compounds based on 13 C NMR chemical shift data. This can be a very useful tool in structure validation, which is one of the steps of automated structure elucidation process. In(More)
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(More)
This paper describes the application of artificial neural nets as an alternative and efficient method for the classification of botanical taxa based on chemical data (chemosystematics). A total of 28,000 botanical occurrences of chemical compounds isolated from the Asteraceae family were chosen from the literature, and grouped by chemical class for each(More)
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