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SAR QSAR Environ Res. 2017 Sep;28(9):735-747. doi: 10.1080/1062936X.2017.1376705.

Machine learning-based models to predict modes of toxic action of phenols to Tetrahymena pyriformis.

Author information

1
a Unidad de Toxicología Experimental , Universidad de Ciencias Médicas de Villa Clara , Santa Clara , Villa Clara , Cuba.
2
b Departament de Biología Funcional i Antropología Física , Universitat de València , Burjassot , Spain.
3
c Departamento de Química Física, Facultad de FarmaciaUnidad de Investigación de Diseño de Fármacos y Conectividad Molecular , Universitat de València , Spain.
4
d Department of Chemistry , McGill University , Montréal , Québec , Canada.
5
e Hanoi University of Pharmacy , Hoan Kiem, Hanoi , Vietnam.
6
f Institut Universitari de Ciència Molecular , Universitat de València, Edifici d'Instituts de Paterna , Valencia , Spain.

Abstract

The phenols are structurally heterogeneous pollutants and they present a variety of modes of toxic action (MOA), including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles, and soft electrophiles. Because it is often difficult to determine correctly the mechanism of action of a compound, quantitative structure-activity relationship (QSAR) methods, which have proved their interest in toxicity prediction, can be used. In this work, several QSAR models for the prediction of MOA of 221 phenols to the ciliated protozoan Tetrahymena pyriformis, using Chemistry Development Kit descriptors, are reported. Four machine learning techniques (ML), k-nearest neighbours, support vector machine, classification trees, and artificial neural networks, have been used to develop several models with higher accuracies and predictive capabilities for distinguishing between four MOAs. They showed global accuracy values between 95.9% and 97.7% and area under Receiver Operator Curve values between 0.978 and 0.998; additionally, false alarm rate values were below 8.2% for training set. In order to validate our models, cross-validation (10-folds-out) and external test-set were performed with good behaviour in all cases. These models, obtained with ML techniques, were compared with others previously reported by other researchers, and the improvement was significant.

KEYWORDS:

Molecular descriptor; QSAR; machine learning technique; mode of toxic action; phenol derivative; pollutant

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