Knowledge discovery and data mining in toxicology

Knowledge discovery and data mining tools are gaining increasing importance for the analysis of toxicological databases. This paper gives a survey of algorithms, capable to derive interpretable models from toxicological data, and presents the most important application areas. The majority of techniques in this area were derived from symbolic machine learning, one commercial product was developed especially for toxicological applications. The main application area is presently the detection of stru cture--activity relationships, very few authors have used these techniques to solve problems in epidemiological and clinical toxicology. Although the discussed algorithms are very flexible and powerful, further r esearch is required to adopt the algorithms to the specific learning proble ms in this area, to develop improved representations of chemical and biolog ical data and to enhance the interpretability of the derived models for tox icological experts.