Hybrid toxicology expert system: Architecture and implementation of a multi-domain hybrid expert system for toxicology

Giuseppina Gini, Vito Testaguzza, Emilio Benfenati, Roberto Todeschini

Research output: Contribution to journalArticle

Abstract

A hybrid expert system prototype using artificial neural networks (ANN) and classical rules has been developed for predicting toxicology of compounds. Modularity was a must for the architecture of the system. The study of chemicals was approached by establishing classes. When appropriate descriptors are calculated for the molecule, the ANN classifier assigns the chemical class to the compound. Then the toxic activity is quantitatively predicted of by one of the trained ANN in the system. After that, a qualitative prediction (active/non-active) is made by a rule-based system, calling only the correct knowledge base (KB) for the assigned class. This last step enabled us to give an explanation of the results. All the rules in the KBs have been obtained with automated learning techniques.

Original languageEnglish
Pages (from-to)135-145
Number of pages11
JournalChemometrics and Intelligent Laboratory Systems
Volume43
Issue number1-2
DOIs
Publication statusPublished - Sep 28 1998

Keywords

  • Artificial neural networks
  • Automated role extraction
  • Expert systems
  • Feature selection
  • QSAR models
  • Toxicology
  • WHIM descriptors

ASJC Scopus subject areas

  • Analytical Chemistry
  • Spectroscopy
  • Statistics and Probability

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