Modeling of the acute toxicity of benzene derivatives by complementary QSAR methods

Carlo Bertinetto, Celia Duce, Roberto Solaro, Maria Rosaria Tiné, Alessio Micheli, Károly Héberger, Ante Miličević, Sonja Nikolić

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9 Citations (Scopus)


A data set containing acute toxicity values (96-h LC50) of 69 substituted benzenes for fathead minnow (Pimephales promelas) was investigated with two Quantitative Structure-Activity Relationship (QSAR) models, either using or not using molecular descriptors, respectively. Recursive Neural Networks (RNN) derive a QSAR by direct treatment of the molecular structure, described through an appropriate graphical tool (variable-size labeled rooted ordered trees) by defining suitable representation rules. The input trees are encoded by an adaptive process able to learn, by tuning its free parameters, from a given set of structure activity training examples. Owing to the use of a flexible encoding approach, the model is target invariant and does not need a priori definition of molecular descriptors. The results obtained in this study were analyzed together with those of a model based on molecular descriptors, i.e. a Multiple Linear Regression (MLR) model using CROatian MultiRegression selection of descriptors (CROMRsel). The comparison revealed interesting similarities that could lead to the development of a combined approach, exploiting the complementary characteristics of the two approaches.

Original languageEnglish
Pages (from-to)1005-1021
Number of pages17
Issue number3
Publication statusPublished - Dec 1 2013


ASJC Scopus subject areas

  • Chemistry(all)
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Applied Mathematics

Cite this

Bertinetto, C., Duce, C., Solaro, R., Tiné, M. R., Micheli, A., Héberger, K., Miličević, A., & Nikolić, S. (2013). Modeling of the acute toxicity of benzene derivatives by complementary QSAR methods. Match, 70(3), 1005-1021.