Predicting Chemical Carcinogenesis Using Structural
Information Only
Third European Conference on the Principles of Data
Mining and Knowledge Discovery, page 360--365. Springer, (September 1999)Abstract
This paper reports on the application of the Strongly
Typed Evolutionary Programming System (STEPS) to the
PTE2 challenge, which consists of predicting the
carcinogenic activity of chemical compounds from their
molecular structure and the outcomes of a number of
laboratory analyses. Most contestants so far have
relied heavily on results of short term toxicity (STT)
assays. Using both types of information made available,
most models incorporate attributes that make them
strongly dependent on STT results. Although such models
may prove to be accurate and informative, the use of
toxicological information requires time cost and in
some cases substantial use of laboratory animals. If
toxicological information only makes explicit,
properties implicit in the molecular structure of
chemicals, then provided a sufficiently expressive
representation language, accurate solutions may be
obtained from the structural information only. Such
solutions may offer more tangible insight into the
mechanistic paths and features that govern chemical
toxicity as well as prediction based on virtual
chemistry for the universe of compounds.
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