title: A novel RBF neural network training methodology to predict toxicity to Vibrio fischeri.
abstract: This work introduces a neural network methodology for developing QSTR predictors of toxicity to Vibrio fischeri. The method adopts the Radial Basis Function (RBF) architecture and the fuzzy means training strategy, which is fast and repetitive, in contrast to most traditional training techniques. The data set that was utilized consisted of 39 organic compounds and their corresponding toxicity values to Vibrio fischeri, while lipophilicity, equalized electronegativity and one topological index were used to provide input information to the models. The performance and predictive ability of the RBF model were illustrated through external validation and various statistical tests. The proposed methodology can be used to successfully model toxicity to Vibrio fischeri for a heterogeneous set of compounds.
authors.affiliation: Laboratory of Organic Chemistry, 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: Haralambos
authors.initials: H
authors.lastname: Sarimveis
authors.forename: Olga
authors.initials: O
authors.lastname: Igglessi-Markopoulou
authors.forename: Alex
authors.initials: A
authors.lastname: Alexandridis
mesh_terms: D048908:Aliivibrio fischeri / Q000187:drug effects*; D008956:Models, Chemical; D016571:Neural Networks, Computer; D009930:Organic Chemicals / Q000633:toxicity*; D021281:Quantitative Structure-Activity Relationship; D014116:Toxicology / Q000379:methods*
chemical_list: D009930:Organic Chemicals