Sources Substructure Searching

A recent literature survey revealed >3,000 piperazine substructure containing compounds accessed by isocyanide-based MCRs. Many of these piperazines are of commercial interest and therefore are described in patents as a primary literature source. Moreover, 430,000 piperazine derivatives are commercially available (according to a substructure search in eMolecules) [1]. Inspecting the general scaffold types in the database of the commercially available piperazine derivatives, it can be speculated that a considerable fraction of them were made by MCR technology. Some examples of piperazine derivatives via isocyanide-based MCRs from patent literature are listed in Table 1. 

Both DIVA and RS3 provide some functionality in terms of substructure searches (SSS), although it is somewhat limited. For example, DIVA searches can only be performed on data that have already been queried from the database ). This pre-queried data need to be readily available to DIVA either via RS3 or as an SD file. In the case of RS3, the inclusion of multiple data sources (e.g., searching the corporate database and an external vendor library) is not trivial. As a result, while DIVA and RS3 are very useful for SSS under certain conditions, they are not as robust when compared to the Pipeline Pilot protocol. 

CDD has developed and deployed a robust preliminary public antimalarial database from five sources which hosts data on approximately 16,000 public compounds. The growth of this database has fostered several key antimalarial discovery collaborations between CDD users. A substructure search for the known chemo-sensitizer substructure led to the identification of hundreds of compounds for laboratory evaluation by the laboratories of... 

Substructure searches in topological (2D) databases of compounds of diverse sources (e.g., commercially available compounds, company database) are extensively used by medicinal chemists to preselect substances for screening. Since the pharmacological action is mediated through the 3D shape of the ligand molecule in the receptor-bound conformation, efforts have been made in recent years to construct low-energy 3D structures from 2D databases. A pharmacophore hypothesis can be derived from a series of known inhibitors and their consensus 3D features. 

For this task, easily accessible properties of mixtures or pure metabolites are compared with literature data. This may be the biological activity spectrum against a variety of test organisms. Widely used also is the comparison of UV [90] or MS data and HPLC retention times with appropriate reference data collections, a method which needs only minimal amounts and affords reliable results. Finally, there are databases where substructures, NMR or UV data and a variety of other molecular descriptors can be searched using computers [91]. The most comprehensive data collection of natural compounds is the Dictionary of Natural Products (DNP) [92], which compiles metabolites from all natural sources, also from plants. More appropriate for dereplication of microbial products, however, is our own data collection (AntiBase [93]) that allows rapid identification using combined structural features and spectroscopic data, tools that are not available in the DNP. 

An additional data source that Mobius allows users to create is the calculated field. A calculated field is simply a computed expression of arbitrary complexity involving one or more existing database fields for example, the ratio of two IC50 values for two different assays. The user can optionally apply a classification method to the output value of a calculated field to categorize the result. For example, the base calculated field could simply consist of a chemical structure column and the classification could assign a chemical template name to the structure based on a set of user-defined substructure query classification rules. As with annotation tables, calculated fields can be associated with project data and assigned security attributes. Both annotation tables and calculated fields are full members of the overall integrated database and can be searched, sorted, and combined with any other data in the database. 

The team decided that the system software should be selected from a source that was well-established in the field of technical information. In addition, if one software and one command language could be used for the entire system, i.e., both text and chemical structure, it would be advantageous to the technical community. Because the thesaurus, a hierachical list of controlled terms, was the key to the text or document file, thesaurus software was also necessary. Search software for chemicals had to have the capability to search by substructure or full structure, by name, by compound number, by molecular formula, and by class descriptor. Continuity in both systems support and staff was a very important consideration. Another criterion was that the system be kept up-to-date with enhancements resulting from ongoing research in information science. 

The interactive use of a structure generator together with a database is demonstrated with Speclnfo/Molgen. Spec-Info/Molgen does not yet contain restriction software the spectroscopist has to introduce those criteria. Two sources can be used to generate structural restrictions Specinfo yields substructure information from spectrum similarity searches, and knowledge of the chemical reaction which leads to the unknown structure provides extra information. Parts of the... 

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