Corporate database

Geological Survey of Western Australia has combined its regional geochemical data with other lithogeochemical information in a corporate database, and delivered the data via a customised web application which combines... 

As profiling plays a key role in lead selection and optimization, data quality, availability, and cost become important considerations. Indeed, all profiling data are critical since these will both support the selection of the best candidates and will be included in a corporate database to support further improvement of the discovery process. 

Furet et al. described the discovery of a novel, potent and selective kinase CK2 inhibitor by a high-throughput docking protocol [250]. A large subset of the Novartis corporate database with 400 000 compounds was flexibly docked using DOCK 4.01 [93, 117]... 

Shemetulskis, N.E., Dunbar, J.B., Jr., Dunbar, B.W., Moreland, D.W., and Humblet, C. Enhancing the diversity of a corporate database using chemical database clustering and analysis. 

Extraction of Building Blocks from Corporate Databases 201... 

The RACHEL software has a far greater problem. While current builder-type software packages contain databases with 1000 components or less, RACHEL can extract upward of500 000 components depending upon the size and diversity of the corporate database. Thus, the number of potential fragment combinations is nearly immeasurable. Clearly, a method is needed to rapidly focus on the appropriate combinations that are likely to satisfy binding requirements. 

The greatest benefit of RACHEL S component extraction method is that a massive property index of the entire corporate database is created. Along with the atomic coordinates of each component, a wealth of chemical information characterizing each building block is stored. Data such as the size of the component, atom composition, connectivity, ring structure, and electrostatic charges are included. As such, a means of rapidly cross-referencing chemical components on demand is available. 

As we have shown in this example, the use of templates and chemical descriptors allows RACHEL to generate chemically diverse ligand derivatives within specific user constraints. In addition, building structures from enriched corporate database fragments... 

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. 

The software utilizes a wide variety of information contained in corporate databases to identify interesting compounds with lead-like features. These features of a compound are grouped into several categories and are combined to create scores that define fairly independent measures of a compound s suitability for follow-up evaluation. These scores are then combined to create a composite score that weights the features according to project team objectives. 

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. 

Most corporate databases of chemical compounds (libraries) are of the 2D type. The databases are managed using software that allows fast registration of new structures, fast retrieval of previously stored compounds, and fast substructure searching. (For more information about chemical database management software, see www.mdl.com or www.daylight.com.)... 

The strength of this model is that investigators are practicing physicians. In terms of patient recruitment, this type of SMO has access to a greater number of patients through its members patient databases. The SMO may supplement this core resource with advertising and its own corporate database, but the majority of the patients are likely to come from the physicians own patient community. 

Shemetulskis et al. [44] describe a method based on clustering that was used to compare two external databases with a corporate database. Each database was clustered independently using the Jarvis-Patrick method [46] representative subsets of each database were chosen and the subsets were then mixed and re-clustered. The number of clusters that contain compounds from only one of the databases was then used as an indication of the degree of overlap between the two databases. A limitation of this approach is the computational effort required to re-cluster the mixed subsets. 

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