Artificial Neural Networks Based on CODES Descriptors in Pharmacology: Identification of Novel Trypanocidal Drugs against Chagas Disease
Nuria E. Campillo,
Juan A. Paez.
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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