Modeling of the Acute Toxicity of Benzene Derivatives by Complementary QSAR Methods

Carlo Bertinetto*, Celia Duce, Roberto Solaro, Maria Rosaria Tine, Alessio Micheli, Karoly Heberger, Ante Milicevic, Sonja Nikolic

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

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
JournalMATCH: COMMUNICATIONS IN MATHEMATICAL AND IN COMPUTER CHEMISTRY
Volume70
Issue number3
Publication statusPublished - 2013
MoE publication typeA1 Journal article-refereed

Keywords

  • RECURSIVE NEURAL-NETWORKS
  • GLASS-TRANSITION TEMPERATURE
  • PREDICTION
  • QSPR
  • REPRESENTATIONS
  • TOXICOLOGY
  • POLYMERS

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