Databases chemical genetics

Figure 3.17 Chemical genetics databases require the annotation of individual compounds with many biologies, in contrast to the more traditional way of capturing the assay results of many compounds against a single biology.

Collaborative research initiatives for the use of chemical genetics to facilitate discovery of potential cancer (or other) therapeutics by the initiative for Chemical Genetics at the National Cancer Institute (50) and to facilitate the discovery of netv targets and drug candidates by the Chemical Genomics Center at the National Institutes of Health (51) have been initiated. These initiatives will result in databases that contain information that most likely will be valuable to the study of cellular signaling networks. 

Another EPA database, contains genetic toxicology test results on over 3000 chemicals. Selectively reviewed for each of the test systems under evaluation. The GENE-TOX data bank is the product of these data review activities... 

Bissantz C, Bernard P, Hibert M, Rognan D. Protein-based virtual screening of chemical databases. II. Are homology models of G-protein coupled receptors suitable targets Proteins Struct Funct Genet 2003 50 5-25. 

There are a number of test systems that use cultured mammalian cells, from both established and primary lines, that now have a large database of tested chemicals in the literature, are relatively rapid, and are feasible to use for genetic toxicity screening. These are discussed in the next section. 

In this case, systematic methods for generation of feasible molecular and mixture candidates have been reported for the design of functional chemical products [see Achenie et al (2003)]. Methods based on database search, total enumeration of feasible candidates (rule-based techniques that avoid a combinatorial explosion), mathematical programming, genetic algorithm, and,... 

Today, most data are entered into corporate databases which consider the need of the user and the purpose of data. They are structured, searchable, contain both raw and metadata. Decision-making tools can mine these databases and if necessary combine data from various sources, including genetic, proteomic, clinical and chemical databases. 

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