The proposal of architecture for chemical splitting to optimize QSAR models for aquatic toxicity

Andrea Colombo, Emilio Benfenati, Mati Karelson, Uko Maran

Research output: Contribution to journalArticlepeer-review


One of the challenges in the field of quantitative structure-activity relationship (QSAR) analysis is the correct classification of a chemical compound to an appropriate model for the prediction of activity. Thus, in previous studies, compounds have been divided into distinct groups according to their mode of action or chemical class. In the current study, theoretical molecular descriptors were used to divide 568 organic substances into subsets with toxicity measured for the 96-h lethal median concentration for the Fathead minnow (Pimephales promelas). Simple constitutional descriptors such as the number of aliphatic and aromatic rings and a quantum chemical descriptor, maximum bond order of a carbon atom divide compounds into nine subsets. For each subset of compounds the automatic forward selection of descriptors was applied to construct QSAR models. Significant correlations were achieved for each subset of chemicals and all models were validated with the leave-one-out internal validation procedure (Rcv 2 ≈ 0.80). The results encourage to consider this alternative way for the prediction of toxicity using QSAR subset models without direct reference to the mechanism of toxic action or the traditional chemical classification.

Original languageEnglish
Pages (from-to)772-780
Number of pages9
Issue number5
Publication statusPublished - Jun 2008


  • Fathead minnow
  • Maximum bond order
  • Multi-linear regression
  • QSAR
  • Toxicity prediction

ASJC Scopus subject areas

  • Environmental Chemistry
  • Environmental Science(all)


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