Editorial [Hot Topic:Structuring Chemical Information for Quicker and More Reliable Drug Safety Assessment (Guest Editor: Romualdo Benigni)]

The removal of a pharmaceutical drug from the market because of unexpected adverse reactions is one of the most dramatic events that may take place during the long process ranging from design to marketing. Several drugs have been withdrawn, or their use subjected to serious restrictions because of various toxicity problems (e.g., valvular heart disease, liver failure, ischemic colitis, and torsade de points) not recognized during pre-clinical and clinical experimentation. The dimension of the problem is impressive: the toxic effects from marketed drugs, even when used appropriately, are estimated to rank among the top ten causes of death in the United States. For this reason, methods that can predict toxic effects at the early stages of drug development are urgently needed. One approach is to exploit the wealth of chemical knowledge to build structure-activity based predictive models for toxicity. As a matter of fact, the contribution offered by the treatment of molecules through computers is much wider than that can be perceived by looking only at the classical structure-activity relationships models. The chemical structure as a chemical identifier has a universally understood meaning and scientific relevance. Chemical structure and chemical concepts (e.g. reactive functional groups, acidity, hydrophobicity, electrophilic reactivity, and free radical formation) provide a common language and framework for exploring the underlying chemical reactivity bases for diverse toxicological outcomes. Hence, chemical structure should be considered an essential identifier and a scientifically useful metric for chemical toxicity databases. Effective linkage of toxicity data with chemical structure information can facilitate and greatly enhance data gathering and hypothesis generation in conjunction with (Q)SAR modeling efforts. This hot topic issue is aimed at providing an insight into the wider landscape of the computerized treatment of molecules for toxicity prediction. Specific to this hot topics issue is the strong belief that one of the most serious problems that hampers the progress of science is the separation between different areas of knowledge and different disciplines. Often, progress in one area is not known to scientists who deal with the same problem, but belong to another discipline. For example, parallel work has been done to build models for predicting the toxicity of pharmaceutical drugs and that of environmental chemicals, with little mutual benefit. Thus in this issue, a special effort has been made to gather contributions from both fields, and to bridge the gap between the two disciplines and to cross-fertilize. In the first mini-review, Remigius Didziapetris et al. overview historic developments and practical implications of property based drug design. The emergence of virtual screening to remove undesirable compounds from consideration prior to their synthesis or acquisition is outlined, and several in silico tools are described. Critical issues on the use of in silico approaches are discussed, and future developments are suggested. In her mini-review, Ana Gallegos presents both theoretical insights and practical applications relative to one of the basic concepts on which the concept of Structure-Activity Relationships relies, i.e., chemical similarity. The paper shows how the heuristic and subjective process of establishing similarities and analogies, when applied to molecules, has produced very diverse and sophisticated formalized treatments. The paper emphasizes that the use of quantitative similarity measures for toxicity modeling and prediction is highly context-dependent, and needs to account for each specific activity or toxicity. The mini-review by Christoph Helma and Jeroen Kazius is on the subject of Artificial Intelligence in data mining and toxicity prediction. It 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. Among others, the paper discusses critically the rapidly expanding field of chemical structure representation (including algorithms for substructure searching). Special emphasis is given to the validation procedures for (Q)SAR models. Chihae Yang et al. review the twin concepts of data bases and data mining. The purposes of toxicity data bases range from.........