title: Prediction of toxicity using a novel RBF neural network training methodology.
abstract: A neural network methodology based on the radial basis function (RBF) architecture is introduced in order to establish quantitative structure-toxicity relationship models for the prediction of toxicity. The dataset used consists of 221 phenols and their corresponding toxicity values to Tetrahymena pyriformis. Physicochemical parameters and molecular descriptors are used to provide input information to the models. The performance and predictive abilities of the RBF models are compared to standard multiple linear regression (MLR) models. The leave-one-out cross validation procedure and validation through an external test set produce statistically significant R2 and RMS values for the RBF models, which prove considerably more accurate than the MLR models. [Figure: see text].
authors.affiliation: School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechniou Str., Zografou Campus, Athens, 15780, Greece.
authors.forename: Georgia
authors.initials: G
authors.lastname: Melagraki
authors.forename: Antreas
authors.initials: A
authors.lastname: Afantitis
authors.forename: Kalliopi
authors.initials: K
authors.lastname: Makridima
authors.forename: Haralambos
authors.initials: H
authors.lastname: Sarimveis
authors.forename: Olga
authors.initials: O
authors.lastname: Igglessi-Markopoulou
mesh_terms: D000818:Animals; D016571:Neural Networks, Computer; D010636:Phenols / Q000737:chemistry / Q000633:toxicity; D013769:Tetrahymena pyriformis / Q000187:drug effects
chemical_list: D010636:Phenols