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Internet Electronic Journal of Molecular Design - IEJMD, ISSN 1538-6414, CODEN IEJMAT
ABSTRACT - Internet Electron. J. Mol. Des. January 2005, Volume 4, Number 1, 9-16

Neural Networks for Secondary Metabolites Prediction in Artemisia Genus (Asteraceae)
Tanja Schwabe, Marcelo J. P. Ferreira, Sandra A. V. Alvarenga, and Vicente P. Emerenciano
Internet Electron. J. Mol. Des. 2005, 4, 9-16

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Abstract:
The chemistry of secondary metabolites is a peculiar field of study due to its complexity and the interest it raises in other fields of pharmacology. The plants of the Asteraceae, one of the largest families of plants, have been intensely studied for this reason and have been resulted in the identification of around 28000 occurrences of substances in the species of the family. The chemistry of the Asteraceae is extremely complex and the great problem with databases compiled from the literature is the lack of knowledge about the precision of the data. Thus, the imprecision of the data leads us to use specific techniques to work with this kind of incomplete data. So, the use of artificial neural networks is very adequate. In the present study we focus attention at the genus Artemisia and work at the infra genus level in order to try to predict the occurrence of chemical substances present in the genus. The methodology applied starts by taking all the information on the genus Artemisia from the database. An entry matrix was assembled with the occurrences of the six most representative chemical classes in the genus: flavonoids, monoterpenes, sesquiterpenes, sesquiterpene lactones, polyacetylenes and coumarins. The training of the network was performed with the statistical package Statsoft using the backpropagation algorithm. The secondary metabolites most frequently present in the genus Artemisia are monoterpenes and sesquiterpene lactones. Since monoterpenes are present in almost all species, this variable is highly correlated to the variable corresponding of the number total of occurrences. Analyzing the variables corresponding to the sesquiterpene lactones, flavonoids and coumarins show that the two previous ones have similar test set and range errors (c.a. 0.20) while for coumarins, the error is the same, but range falls to half of that. The results presented show that the mechanism of the neural networks may be effective to predict the occurrence of secondary metabolites in plant genera if an adequate network is used. In this study we show too the application of the artificial neural networks in the chemistry of natural products, a field in which the numerical precision is very small.

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