Evaluating the applicability domain in the case of classification predictive models for carcinogenicity based on the counter propagation artificial neural network

Natalja Fjodorova, Marjana Novič, Alessandra Roncaglioni, Emilio Benfenati

Research output: Contribution to journalArticle

Abstract

The applicability domain (AD) of models developed for regulatory use has attached great attention recently. The AD of quantitative structure-activity relationship (QSAR) models is the response and chemical structure space in which the model makes predictions with a given reliability. The evaluation of AD of regressions QSAR models for congeneric sets of chemicals can be find in many papers and books while the issue about metrics for the evaluation of an AD for the non-linear models (like neural networks) for the diverse set of chemicals represents the new field of investigations in QSAR studies. The scientific society is standing before the challenge to find out reliable way for the evaluation of an AD of non linear models. The new metrics for the evaluation of the AD of the counter propagation artificial neural network (CP ANN) models are discussed in the article: the Euclidean distances between an object (molecule) and the corresponding excited neuron of the neural network and between an object (molecule) and the representative object (vector of average values of descriptors). The investigation of the training and test sets chemicals coverage in the descriptors space was made with the respect to false predicted chemicals. The leverage approach was used to compare non linear (CP ANN) models with linear ones.

Original languageEnglish
Pages (from-to)1147-1158
Number of pages12
JournalJournal of Computer-Aided Molecular Design
Volume25
Issue number12
DOIs
Publication statusPublished - Dec 2011

Keywords

  • (Quantitative) structure-activity relationship ((Q)SAR)
  • Applicability domain (AD)
  • Counter propagation artificial neural network (CP ANN)
  • Leverage approach
  • Rodent carcinogenicity

ASJC Scopus subject areas

  • Drug Discovery
  • Physical and Theoretical Chemistry
  • Computer Science Applications

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