Modeling toxicity by using supervised Kohonen neural networks

Paolo Mazzatorta, Marjan Vračko, Aneta Jezierska, Emilio Benfenati

Research output: Contribution to journalArticlepeer-review


Counterprogation neural network is shown to be a powerful and suitable tool for the investigation of toxicity. This study mined a data set of 568 chemicals. Two hundred eighty-two objects were used as the training set and 286 as the test set. The final model developed presents high performances on the data set R2 = 0.83 (R2 = 0.97 on the training set, R2 = 0.59 on the test set). This technique distinguishes itself also for the ability to give to the expert two-dimensional maps suitable for the study of the distribution/clustering of the data and the identification of outliers.

Original languageEnglish
Pages (from-to)485-492
Number of pages8
JournalJournal of Chemical Information and Computer Sciences
Issue number2
Publication statusPublished - Mar 2003

ASJC Scopus subject areas

  • Chemistry(all)
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems


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