A toxic substance exerts its toxicity through interaction (covalent bonding or oxidation) with a cellular macromolecule, such as a protein or DNA. This causes changes in the normal cellular biochemistry and physiology eliciting toxic effects. Occasionally, the toxicophore requires bioactivation, modified by an enzyme, to produce a more reactive chemical species that is able to covalently bind to cellular macromolecules. Generally, different chemical compounds that contain the same toxicophore elicit similar toxic effects within the same organ system or area of the body.
Study of toxicophores focuses on chemical design which either predicts and avoids toxicophores early in the process, or identifies and removes them later in the process. Both techniques, in silico (predictive) and a posteriori (experimental), are active areas of chemoinformatics research and development, within the field known as Computational Toxicology. For example, in the United States, the EPA's National Center for Computational Toxicology sponsors several toxicity databases based on predictive modeling as well as high-throughput screening experimental methods.
- Williams, D.P.; Naisbitt, D.J. (2002). Toxicophores: Groups and Metabolic Routes Associated with Increased Safety Risk. Curr. Opin. Drug. Discov. Devel. pp. 104–115.
- Seal, Abhik; Passi, Anurag; Jaleel, Abdul; Wild, David J Wild. (2012). In-silico predictive mutagenicity model generation using supervised learning approaches. Journal of Cheminformatics. pp. 4:10.
- "Computational Toxicology: Superfund Research Program". National Institute of Environmental Health Sciences. 2009.
- "About the National Center for Computational Toxicology (NCCT)". Research Triangle Park, NC. 2005.
- "ToxCast: Advancing the next generation of chemical safety evaluation". Retrieved March 10, 2014.
- "ACToR: Aggregated Computational Toxicology Resource". Retrieved March 10, 2014.
- "Distributed Structure-Searchable Toxicity (DSSTox) Database Network". Retrieved March 10, 2014.
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