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

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

Izvornik
MATCH Communications in Mathematical and in Computer Chemistry (0340-6253) 70 (2013); 1005-1021

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
modeling ; toxicity ; benzene derivatives

Sažetak
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 structureactivity 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.

Izvorni jezik
Engleski

Znanstvena područja
Kemija

POVEZANOST RADA