Prediction of atmospheric degradation data for POPs by gene expression programming

SAR QSAR Environ Res. 2008;19(5-6):465-79. doi: 10.1080/10629360802348845.

Abstract

Quantitative structure-activity relationship models for the prediction of the mean and the maximum atmospheric degradation half-life values of persistent organic pollutants were developed based on the linear heuristic method (HM) and non-linear gene expression programming (GEP). Molecular descriptors, calculated from the structures alone, were used to represent the characteristics of the compounds. HM was used both to pre-select the whole descriptor sets and to build the linear model. GEP yielded satisfactory prediction results: the square of the correlation coefficient r(2) was 0.80 and 0.81 for the mean and maximum half-life values of the test set, and the root mean square errors were 0.448 and 0.426, respectively. The results of this work indicate that the GEP is a very promising tool for non-linear approximations.

MeSH terms

  • Artificial Intelligence
  • Atmospheric Pressure
  • Environmental Pollutants / chemistry
  • Environmental Pollutants / metabolism*
  • Environmental Pollutants / toxicity
  • Gene Expression Regulation / physiology*
  • Hazardous Substances
  • Linear Models
  • Organic Chemicals / chemistry
  • Organic Chemicals / metabolism*
  • Organic Chemicals / toxicity
  • Predictive Value of Tests*
  • Quantitative Structure-Activity Relationship*

Substances

  • Environmental Pollutants
  • Hazardous Substances
  • Organic Chemicals