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

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

Abstrakti

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.

AlkuperäiskieliEnglanti
Sivut1005-1021
Sivumäärä17
JulkaisuMATCH: COMMUNICATIONS IN MATHEMATICAL AND IN COMPUTER CHEMISTRY
Vuosikerta70
Numero3
TilaJulkaistu - 2013
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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