Binary classification models for endocrine disrupter effects mediated through the estrogen receptor

A. Roncaglioni, N. Piclin, M. Pintore, E. Benfenati

Research output: Contribution to journalArticlepeer-review


Endocrine disrupters (EDs) form an interesting field of application attracting great attention in the recent years. They represent a number of exogenous substances interfering with the function of the endocrine system, including the interfering with developmental processes. In particular EDs are mentioned as substances requiring a more detailed control and specific authorization within REACH, the new European legislation on chemicals, together with other groups of chemicals of particular concern. QSAR represents a challenging method to approach data gap which is foreseen by REACH. The aim of this study was to provide an insight into the use of QSAR models to address ED effects mediated through the estrogen receptor (ER). New predictive models were derived to assess estrogenicity for a very large and heterogeneous dataset of chemical compounds. QSAR binary classifiers were developed based on different data mining techniques such as classification trees, decision forest, fuzzy logic, neural networks and support vector machines. The focus was given to multiple endpoints to better characterize the effects of EDs evaluating both binding (RBA) and transcriptional activity (RA). A possible combination of the models was also explored. A very good accuracy was reached for both RA and RBA models (higher than 80%).

Original languageEnglish
Pages (from-to)697-733
Number of pages37
JournalSAR and QSAR in Environmental Research
Issue number7-8
Publication statusPublished - Oct 2008


  • Classification tree
  • Endocrine disrupters
  • Estrogen receptor
  • Fuzzy logic
  • Neural network
  • SVM

ASJC Scopus subject areas

  • Molecular Medicine
  • Bioengineering
  • Drug Discovery


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