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Title

Artificial neural networks based on CODES descriptors in pharmacology: Identification of novel trypanocidal drugs against chagas disease

AuthorsGuerra, Ángela ; González-Naranjo, Pedro ; Campillo, Nuria E. ; Cerecetto, Hugo; González, Mercedes; Páez, Juan A.
Keywordstrypanocidal
CODES
Chagas disease
QSAR
Trypanosoma cruzi
compounds
in silico
molecules
neural network
Pharmacology
Issue Date2013
PublisherBentham Science Publishers
CitationCurrent Computer-Aided Drug Design 9: 130- 140 (2013)
AbstractA 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. © 2013 Bentham Science Publishers.
URIhttp://hdl.handle.net/10261/95347
DOIhttp://dx.doi.org/10.2174/1573409911309010012
ISSN1573-4099
E-ISSN1875-6697
Appears in Collections:(IQM) Artículos
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