title: An automated group contribution method in predicting aquatic toxicity: the diatomic fragment approach.
abstract: We developed a group contribution method (GCM) to correlate acute toxicity (96 h LC50) for the fathead minnow (Pimephales promelas) for 607 organic chemicals. Unlike most of the existing methods, the new one makes no use of predefined groups as descriptors. A simple general rule is proposed to break down any molecule into diatomic fragments. The entire data set was partitioned three times. Each time, a training set and a test set were obtained with a ratio of 2:1. For each partition quantitative structure-activity relationship, models were developed using Powell's minimization method, multilinear regression, neural networks, and partial least squares. The GCM method achieved a good correlation of the data for both training and test sets, regardless of the partition considered. The method is therefore robust and can be generally applied. Further model improvements are described.
authors.affiliation: Institute for Pharmacological Research Mario Negri, IRFMN, via Eritrea 62, 20157 Milano, Italy. casalegno@marionegri.it
authors.forename: Mosé
authors.initials: M
authors.lastname: Casalegno
authors.forename: Emilio
authors.initials: E
authors.lastname: Benfenati
authors.forename: Guido
authors.initials: G
authors.lastname: Sello
mesh_terms: D000818:Animals; D003530:Cyprinidae; D007928:Lethal Dose 50; D016571:Neural Networks, Computer; D012044:Regression Analysis; D013329:Structure-Activity Relationship; D018675:Toxicity Tests / Q000379:methods*