Comparison of in silico models for prediction of Daphnia magna acute toxicity

A. Golbamaki, A. Cassano, A. Lombardo, Y. Moggio, M. Colafranceschi, E. Benfenati

Research output: Contribution to journalArticlepeer-review


Eight in silico modelling packages were evaluated and compared for the prediction of Daphnia magna acute toxicity from the viewpoint of the European legislation on chemicals, REACH. We tested the following models: Discovery Studio (DS) TOPKAT, ACD/Tox Suite, ADMET Predictor™, ECOSAR (Ecological Structure Activity Relationships), TerraQSAR™, T.E.S.T. (Toxicity Estimation Software Tool) and two models implemented in VEGA on 480 industrial compounds for 48-h median lethal concentrations (LC50) to D. magna, matching them with experimental values. The quality of the estimates was compared using a standard statistical review and an additional classification approach in which the hazard predictions were grouped using well-defined regulatory criteria. The regression parameters, correlation coefficient being the most influential, showed that four models (ADMET Predictor™, DS TOPKAT, TerraQSAR™ and VEGA DEMETRA) had similar reliability. These performed better than the others, but the coefficient of determination was still low (r2 around 0.6), considering that at least half the predicted compounds were inside the training sets. Additionally, we grouped the results in four defined toxicity classes. TerraQSAR™ gave 60% of correct classifications, followed by DS TOPKAT, ADMET Predictor™ and VEGA DEMETRA, with 56%, 54% and 48%, respectively. These results highlight the challenges associated with developing reliable and easily applied acceptability criteria for the regulatory use of QSAR models to D. magna acute toxicity.

Original languageEnglish
Pages (from-to)673-694
Number of pages22
JournalSAR and QSAR in Environmental Research
Issue number8
Publication statusPublished - 2014


  • aquatic acute toxicity
  • Daphnia magna
  • in silico models
  • LC
  • QSAR

ASJC Scopus subject areas

  • Molecular Medicine
  • Bioengineering
  • Drug Discovery
  • Medicine(all)


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