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


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 pbkp_rediction, can be used. In this work, several QSAR models for the pbkp_rediction 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 pbkp_redictive 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.

Año de publicación:

2017

Keywords:

  • molecular descriptor
  • Pollutant
  • phenol derivative
  • QSAR
  • Machine learning technique
  • mode of toxic action

Fuente:

scopusscopus

Tipo de documento:

Article

Estado:

Acceso restringido

Áreas de conocimiento:

  • Ecología
  • Aprendizaje automático
  • Toxicología

Áreas temáticas:

  • Ciencias de la computación