Applying the Subdue Substructure Discovery System to the Chemical Toxicity Domain

Ravindra N. Chittimoori, Lawrence B. Holder and Diane J. Cook, University of Texas at Arlington

The ever-increasing number of chemical compounds added every year has not been accompanied by a similar growth in our ability to analyze and classify these compounds. The problem of prevention of cancer caused by many of these chemicals has been of great scientific and humanitarian value. The use of AI discovery tools for predicting chemical toxicity is being investigated. The basic idea behind the work is to obtain structure-activity representation (SARs), which relates molecular structures to cancerous activity. The data is obtained from the U.S National Toxicology Program conducted by the National Institute of Environmental Health Sciences (NIEHS). A general approach to automatically discover repetitive substructures from the datasets is outlined by this research. Relevant SARs are identified using the Subdue substructure discovery system that discovers commonly occurring substructures in a given set of compounds. The best substructure given by Subdue is used as a pattern indicative of cancerous activity.

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