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Curr Comput Aided Drug Des. 2013 Mar;9(1):130-40.

Artificial neural networks based on CODES descriptors in pharmacology: identification of novel trypanocidal drugs against Chagas disease.

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1
Instituto de Química Médica/CSIC, Juan de la Cierva 3, Madrid, Spain. jpaez@iqm.csic.es

Abstract

A supervised artificial neural network model has been developed for the accurate prediction of the anti-T. cruzi activity of heterogeneous series of compounds. A representative set of 72 compounds of wide structural diversity was chosen in this study. The definition of the molecules was achieved from an unsupervised neural network using a new methodology, CODES program. This program codifies each molecule into a set of numerical parameters taking into account exclusively its chemical structure. The final model shows high average accuracy of 84% (training performance) and predictability of 77% (external validation performance) for the 4:4:1 architecture net with different training set and external prediction test. This approach using CODES methodology represents a useful tool for the prediction of pharmacological properties. CODES© is available free of charge for academic institutions.

PMID:
23286783
[Indexed for MEDLINE]
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