A new hybrid system of QSAR models for predicting bioconcentration factors (BCF)

Chunyan Zhao, Elena Boriani, Antonio Chana, Alessandra Roncaglioni, Emilio Benfenati

Research output: Contribution to journalArticlepeer-review


The aim was to develop a reliable and practical quantitative structure-activity relationship (QSAR) model validated by strict conditions for predicting bioconcentration factors (BCF). We built up several QSAR models starting from a large data set of 473 heterogeneous chemicals, based on multiple linear regression (MLR), radial basis function neural network (RBFNN) and support vector machine (SVM) methods. To improve the results, we also applied a hybrid model, which gave better prediction than single models. All models were statistically analysed using strict criteria, including an external test set. The outliers were also examined to understand better in which cases large errors were to be expected and to improve the predictive models. The models offer more robust tools for regulatory purposes, on the basis of the statistical results and the quality check on the input data.

Original languageEnglish
Pages (from-to)1701-1707
Number of pages7
Issue number11
Publication statusPublished - Dec 2008


  • Bioconcentration factors
  • Hybrid model
  • QSAR validation

ASJC Scopus subject areas

  • Environmental Chemistry
  • Chemistry(all)


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