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Current Computer-Aided Drug Design

Editor-in-Chief

ISSN (Print): 1573-4099
ISSN (Online): 1875-6697

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 and Juan A. Paez

Volume 9, Issue 1, 2013

Page: [130 - 140] Pages: 11

DOI: 10.2174/1573409911309010012

Price: $65

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.

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


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