An automated group contribution method in predicting aquatic toxicity: The diatomic fragment approach

Mosé Casalegno, Emilio Benfenati, Guido Sello

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)740-746
Number of pages7
JournalChemical Research in Toxicology
Volume18
Issue number4
DOIs
Publication statusPublished - Apr 2005

ASJC Scopus subject areas

  • Drug Discovery
  • Organic Chemistry
  • Chemistry(all)
  • Toxicology
  • Health, Toxicology and Mutagenesis

Fingerprint Dive into the research topics of 'An automated group contribution method in predicting aquatic toxicity: The diatomic fragment approach'. Together they form a unique fingerprint.

  • Cite this