title: Artificial neural networks based on CODES descriptors in pharmacology: identification of novel trypanocidal drugs against Chagas disease.
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.
authors.affiliation: Instituto de Química Médica/CSIC, Juan de la Cierva 3, Madrid, Spain. jpaez@iqm.csic.es
authors.forename: Angela
authors.initials: A
authors.lastname: Guerra
authors.forename: Pedro
authors.initials: P
authors.lastname: Gonzalez-Naranjo
authors.forename: Nuria E
authors.initials: NE
authors.lastname: Campillo
authors.forename: Hugo
authors.initials: H
authors.lastname: Cerecetto
authors.forename: Mercedes
authors.initials: M
authors.lastname: González
authors.forename: Juan A
authors.initials: JA
authors.lastname: Páez
mesh_terms: D014355:Chagas Disease / Q000188:drug therapy*; D017076:Computer-Aided Design; D015195:Drug Design; D006801:Humans; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship; D012984:Software; D014344:Trypanocidal Agents / Q000737:chemistry* / Q000494:pharmacology*; D014349:Trypanosoma cruzi / Q000187:drug effects*
chemical_list: D014344:Trypanocidal Agents