Classification of a Naïve Bayesian Fingerprint model to predict reproductive toxicity$

M Marzo, E Benfenati

Research output: Contribution to journalArticlepeer-review

Abstract

Using data from the Leadscope database and Procter and Gamble researchers (1172 compounds after data curation) a new classification model to predict reproductive toxicity was developed. The model is based on Naïve Bayesian methods that use the fingerprint "extended connectivity fingerprint 2". Bits generated by the fingerprint are used from the models as descriptors to discriminate between the two classes. This technique permits the creation of a model without the use of descriptors. After a study on the probability scores, the Naïve Bayesian Fingerprint model shows a good performance on reproductive toxicity. The Matthews Correlation Coefficient value was ≥0.4 in validation. The development of new models to predict complex endpoints such as reproductive toxicity is increasingly requested, with reference also to the REACH legislation in Europe or TSCA in the USA.

Original languageEnglish
Pages (from-to)631-645
Number of pages15
JournalSAR and QSAR in Environmental Research
Volume29
Issue number8
DOIs
Publication statusPublished - Aug 2018

Keywords

  • Animals
  • Bayes Theorem
  • Mice
  • Models, Molecular
  • Quantitative Structure-Activity Relationship
  • Rats
  • Reproduction/drug effects
  • Toxicity Tests

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