Accurate prediction of aquatic toxicity of aromatic compounds based on Genetic Algorithm and Least Squares Support Vector Machines

Beilei Lei, Jiazhong Li, Huanxiang Liu, Xiaojun Yao

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

Quantitative Structure - Toxicity Relationship (QSTR) plays an important role in ecotoxicology for its fast and practical ability to assess the potential negative effects of chemicals. The aim of this investigation was to develop accurate QSTR models for the aquatic toxicity of a large set of aromatic compounds through the combination of Least Squares Support Vector Machines (LS-SVMs) and a Genetic Algorithm (GA). Molecular descriptors calculated by DRAGON package and log P were used to describe the molecular structures. The most relevant descriptors used to build QSTR models were selected by a GA-Variable Subset Selection procedure. Multiple Linear Regression (MLR) and nonlinear LS-SVMs methods were employed to build QSTR models. The predictive ability of the derived models was validated using both the test set, selected from the whole data set by the Kennard-Stone Algorithm, and an external prediction set. The model applicability domain was checked by the leverage approach and the external prediction set was used to verify the predictive reliability of the models. The results indicated that the proposed QSTR models are robust and satisfactory, and can provide a feasible and promising tool for the rapid assessment of the toxicity of chemicals.

Original languageEnglish
Pages (from-to)850-865
Number of pages16
JournalQSAR and Combinatorial Science
Volume27
Issue number7
DOIs
Publication statusPublished - Jul 2008
Externally publishedYes

Keywords

  • Applicability domain
  • Aquatic toxicity
  • Genetic algorithm
  • Kennard - Stone algorithm
  • Least squares support vector machines
  • Quantitative structure - toxicity relationships

Fingerprint

Dive into the research topics of 'Accurate prediction of aquatic toxicity of aromatic compounds based on Genetic Algorithm and Least Squares Support Vector Machines'. Together they form a unique fingerprint.

Cite this