Discovering Substructures in the Chemical Toxicity Domain

Ravindra N. Chittimoori, Lawrence B. Holder, and Diane J. Cook

The ever-increasing number of chemical compounds in use each year has not been accompanied by a similar growth in our ability to analyze and classify these compounds. Prevention of cancer caused by many of these chemicals is 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 relate 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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