Prediction of aquatic toxicity of benzene derivatives using molecular descriptor from atomic weighted vectors

Yoan Martínez-López, Stephen J. Barigye, Oscar Martínez-Santiago, Yovani Marrero-Ponce, James Green, Juan A. Castillo-Garit

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Several descriptors from atom weighted vectors are used in the prediction of aquatic toxicity of set of organic compounds of 392 benzene derivatives to the protozoo ciliate Tetrahymena pyriformis (log(IGC50)−1). These descriptors are calculated using the MD-LOVIs software and various Aggregation Operators are examined with the aim comparing their performances in predicting aquatic toxicity. Variability analysis is used to quantify the information content of these molecular descriptors by means of an information theory-based algorithm. Multiple Linear Regression with Genetic Algorithms is used to obtain models of the structure–toxicity relationships; the best model shows values of Q2 = 0.830 and R2 = 0.837 using six variables. Our models compare favorably with other previously published models that use the same data set. The obtained results suggest that these descriptors provide an effective alternative for determining aquatic toxicity of benzene derivatives.

Original languageEnglish
Pages (from-to)314-321
Number of pages8
JournalEnvironmental Toxicology and Pharmacology
Volume56
DOIs
StatePublished - Dec 2017

Keywords

  • Aggregation operator
  • Aquatic toxicity
  • Atom weighted vector
  • Molecular descriptor
  • Multiple linear regression
  • Variability analysis

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