Artificial Neural Networks Based on CODES Descriptors in Pharmacology: Identification of Novel Trypanocidal Drugs against Chagas Disease

Author(s): Angela Guerra, Pedro Gonzalez-Naranjo, Nuria E. Campillo, Hugo Cerecetto, Mercedes Gonzalez, Juan A. Paez

Journal Name: Current Computer-Aided Drug Design

Volume 9 , Issue 1 , 2013

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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.

Keywords: Chagas disease, CODES, in silico, neural network, QSAR, Trypanosoma cruzi, trypanocidal, Pharmacology, molecules, compounds

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Article Details

Year: 2013
Published on: 27 January, 2013
Page: [130 - 140]
Pages: 11
DOI: 10.2174/1573409911309010012

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