Comparative study to predict toxic modes of action of phenols from molecular structures

• Tipo: Artigo em Revista Científica Internacional
• Data: 2013

Autores

Brito Sanchez, Y (Autor) Outra; Castillo Garit, JA (Autor) Outra; Le Thi Thu, H (Autor) Outra; Gonzalez Madariaga, Y (Autor) Outra; Torrens, F (Autor) Outra; Marrero Ponce, Y (Autor) Outra; Rodriguez Borges, JE (Autor) FCUP

Revista

Título: SAR and QSAR in Environmental Research

Vol. 24

Páginas: 235-251

ISSN: 1062-936X

Editora: Taylor & Francis

Indexação

ISI Web of Knowledge - 22 Citações

Scopus - 23 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Química

Outras Informações

ID Authenticus: P-005-2YT

DOI: 10.1080/1062936x.2013.766260

Abstract (EN): Quantitative structureactivity relationship models for the prediction of mode of toxic action (MOA) of 221 phenols to the ciliated protozoan Tetrahymena pyriformis using atom-based quadratic indices are reported. The phenols represent a variety of MOAs including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles and soft electrophiles. Linear discriminant analysis (LDA), and four machine learning techniques (ML), namely k-nearest neighbours (k-NN), support vector machine (SVM), classification trees (CTs) and artificial neural networks (ANNs), have been used to develop several models with higher accuracies and predictive capabilities for distinguishing between four MOAs. Most of them showed global accuracy of over 90%, and false alarm rate values were below 2.9% for the training set. Cross-validation, complementary subsets and external test set were performed, with good behaviour in all cases. Our models compare favourably with other previously published models, and in general the models obtained with ML techniques show better results than those developed with linear techniques. We developed unsupervised and supervised consensus, and these results were better than our ML models, the results of rule-based approach and other ensemble models previously published. This investigation highlights the merits of ML-based techniques as an alternative to other more traditional methods for modelling MOA.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 17