Use of Statistical and Neural Net Approaches in Predicting Toxicity of Chemicals

Subhash C Basak, Gregory D. Grunwald, Brian D Gute, Krishnan Balasubramanian, David Opitz

Research output: Contribution to journalArticlepeer-review

75 Scopus citations

Abstract

Hierarchical quantitative structure-activity relationships (H-QSAR) have been developed as a new approach in constructing models for estimating physicochemical, biomedicinal, and toxicological properties of interest. This approach uses increasingly more complex molecular descriptors in a graduated approach to model building. In this study, statistical and neural network methods have been applied to the development of H-QSAR models for estimating the acute aquatic toxicity (LC50) of 69 benzene derivatives to Pimephales promelas (fathead minnow). Topostructural, topochemical, geometrical, and quantum chemical indices were used as the four levels of the hierarchical method. It is clear from both the statistical and neural network models that topostructural indices alone cannot adequately model this set of congeneric chemicals. Not surprisingly, topochemical indices greatly increase the predictive power of both statistical and neural network models. Quantum chemical indices also add significantly to the modeling of this set of acute aquatic toxicity data.

Original languageEnglish (US)
Pages (from-to)885-890
Number of pages6
JournalJournal of chemical information and computer sciences
Volume40
Issue number4
DOIs
StatePublished - Jan 1 2000

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