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

Abstract

Quantitative structure–activity 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.

Keywords:

• atom-based quadratic indices
• machine learning technique
• quantitative structure–toxicity relationship
• mode of toxic action
• phenol derivative

Acknowledgement

J.A. Castillo-Garit thanks the FCUP programme ‘Bolsa para Cientista Convidado’ for a fellowship to work at University of Porto in 2010. Y. Marrero-Ponce thanks the programme ‘Estades Temporals per an Investigadors Convidats’ for a fellowship to work at Valencia University in 2012. This work was also partially supported by Spanish Ministry of Science and Innovation (MICINN, project reference: SAF2009-10399). We sincerely thank Dr. H.J. Verhaar for providing a PDF copy of his work, which significantly contributed to the development of this paper. The authors also thank Mr. Stephen Jones Barigye for the English revision of the manuscript.