Proceedings:
Predictive Toxicology of Chemicals: Experiences and Impact of AI Tools
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Predictive Toxicology of Chemicals: Experiences and Impact of AI Tools
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Abstract:
Similarity is a powerful tool for compound comparison and can be seen as a good method to predict toxicity. One similarity measure and its application to complex problem solving are described. The importance of the determination of compound toxicity is a fundamental requisite for the introduction of new chemicals into daily use. However, the cost, in terms of both time and money, of an accurate experimental determination forbids an uncontrolled application of well established tests. In addition, the recent efforts oriented to decrease the number of animal tests because their cost and their relative reliability for human toxicity prediction have stimulated the research towards alternative approaches. (Polloth and Mangelsdorf 1997) Among these, theoretical predictions based on the correlation between a structure and its activity represent a powerful method for the selection of toxic candidates which can be then accurately tested by experiments. In order to assess the possibility of a correlation two requisites are needed: the availability of experimental toxicity data and a method for describing structures. Clearly, it is the second aspect that we are concerned with. The description of chemical structures is implicitly a modelling activity. In fact, any representation of molecules must use a "pictorial" description of their structures. The difference is often related to the explicit or implicit way of performing the modelling activity. A second aspect that is fundamental is the understanding that there is not an universal mode of description; on the contrary, it is common to have different methods depending on the current application. In conclusion, we are going to introduce a particular method for describing structures and a system to compare structures, closing with a special attention to their potential application also to the toxicity field.
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Predictive Toxicology of Chemicals: Experiences and Impact of AI Tools