Artificial Intelligence and Data Mining for Toxicity Prediction
Tools for artificial intelligence and data mining can derive (Quantitative) Structure-Activity Relationships ((Q)SARs) for toxicity in an objective and reproducible manner. This review provides a conceptual description of the most important data mining algorithms for the identification of chemical features and the extraction of relationships between these descriptors and toxic activities. We will discuss the compliance of these techniques with the OECD guidelines for (Q)SAR requirements as well as performance implications. Special emphasis will be given to validation procedures for (Q)SAR models.
Keywords: Predictive toxicology, QSAR, artificial intelligence, data mining, machine learning, pattern recognition, datadriven, learning, chemoinformatics
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