Database organization

Huijbregts M, Margni M, van de Meent D, Jolliet O, Rosenbaum RK, McKone T, Hauschild M (2010) USEtox Chemical-specific database organics. http //www.usetox.org/(2010-04-07)... 

Comparisons with the Screen Approach. The algorithm used by screen systems is a special case of our algorithm, the difference between the screen approach and this approach is in the number of levels allowed in the database organization and not in the retrieval algorithm. 

Martin, James In "Computer Database Organization" Prentice-Hall, Inc. Englewood Cliffs, New Jersey, 1975. 

Database organic compounds (carbon containing molecules with up to 1000 atoms) ... 

The main purpose of this chapter, then, is to describe the mechanics of database use the processes of search, retrieval and data organization that are essential precursors to any experiment in structure correlation. In particular, we show that a clear understanding of database organization and information content is vital to a successful experiment of this kind. We illustrate these points by indicating a variety of pitfalls that may beset the unwary and show how they can be circumvented by careful... 

The state of the art is described in many excellent and comprehensive books [4-29] and general review articles [30-70], The reference list gives only the most prominent and best known reviews. Additional and highly recommendable information sources for the synthetic chemist are the Warwick Biotransformation Abstracts and the associated electronic database. Contact address H.G. Crout, Warwick Biotransformation Club Database, Organic Chemical Institute, University of Warwick, UK (service for members only). The two CD-ROMs Biotransformation (K. Kieslich and the Warwick Biotransformation Club) and BioCatalysis (H.L. Holland and B. Jones) are available from Chapman Hall (London, 1996). Practical examples for preparative biotransformations (with checked procedures)... 

One of the oldest types of database and still the most widespread. A relational database organizes the stored data items into tables in which the columns are fields of a specific type and the rows are data entries. The typical query language for relational databases is SQL (systems query language), which is powerful, well defined and, within the domain of the SQL standard, independent of the database vendor. See. Full-text Database Object-oriented Databa.se and Object-Relational Database. 

Usually an Internet user learns after a while where to go for specific information The Browser provides a bookmark tlmction where all these addresses can be organized by different subjects Typical bookmark folders are Societies, Companies, Journals, Institutes, Databases, Search engines The NDTnet Journal provides a virtual library that makes it easy for newcomers to find NDT sites. Unfortunately, some sites provide a lot of information but do not offer a search function. 

The ROSDAL (Representation of Organic Structures Description Arranged Linearly) syntax was developed by S. Welford, J. Barnard, and M.F. Lynch in 1985 for the Beilstein Institute. This line notation was intended to transmit structural information between the user and the Beilstein DIALOG system (Beilstein-Ohlme) during database retrieval queries and structure displays. This exchange of structure information by the ROSDAL ASCII character string is very fast. 

Figure 5-2. The database(s) (DB) with organized data and metadata are part of the Database SysteiTi (DBS), which is managed by the Database Management System (DBMS).

Figure 5-3. a) Main organization of a database or container the basic units of a field are bits and bytes, b) Example of data organization in a flat-file. 

Most database users do not know how the data are organized in a database system (DBS) they depend solely on the application programs. This is sufficient for most database searches where users can receive large amounts of results quickly and easily, e.g., on literature or other information. Nevertheless, a basic knowledge on where and how to find deeper or more detailed information is quite useful. Due to their complex nature, comprehensive searches (e.g., for processes or patents) are not recommended for beginners. However, most local (in-house), online, and CD-ROM databases provide extensive tutorials and help functions that are specific to the database, and that give a substantial introduction into database searching. 

Typical examples of hierarchical database systems arc the file system of personal computers or the organization of parts (e.g., a construction plan). In the case of car parts, the objects (e.g. B = rear suspension, E = right wheel, J = rim, K = screw) are... 

The characteristic of a relational database model is the organization of data in different tables that have relationships with each other. A table is a two-dimensional consti uction of rows and columns. All the entries in one column have an equivalent meaning (c.g., name, molecular weight, etc. and represent a particular attribute of the objects (records) of the table (file) (Figure 5-9). The sequence of rows and columns in the tabic is irrelevant. Different tables (e.g., different objects with different attributes) in the same database can be related through at least one common attribute. Thus, it is possible to relate objects within tables indirectly by using a key. The range of values of an attribute is called the domain, which is defined by constraints. Schemas define and store the metadata of the database and the tables. 

The user is often more interested in the contents than in the technical organization of databases. The wide variety of data allows the classification of databases in chemistry into literature, factual (alphanumeric), and structural types (Figure 5-10) [12, 13). 

Beilstein and Gmelin are the world s largest factual databases in chemistiy. Beil-stein contains facts and structures relating to organic chemistry, whereas Gmelin provides information on inorganic, coordination, and organomctallic compounds. 

The Beilstein database [20] has more than 8.3 million (October, 2002) organic substance records from the Beilstein Handbook and abstracted from about 180 journals in organic chemistry from 1779 to the present. AH documents are critically evaluated and peer-reviewed. 

The two major databases containing information obtained from X-ray structure analysis of small molecules are the Cambridge Structural Database (CSD) [25] and the Inorganic Crystal Structure Database (ICSD) [26] both are available as in-house versions. CSD provides access to organic and organometallic structures (mainly X-ray structures, with some structures from neutron diffraction), data which are mostly unpublished. The ICSD contains inorganic structures. 

GenBank [32] is a text-numeric database of genetic sequences with more than 28 billion bases in 22 million sequences (January, 2003) from genetic research. The collection of all publidy available sequences is annotated with information such as sequence description, source organism, sequence length, or references. The database, estabhshed in 1967, is updated daily and produced by the National Center for Biotechnology Information (USA). 

In this section, the basic concepts of reaction retrieval are explained. The first example is concerned with finding an efficient way to reduce a 3-methy]cydohex-2-cnonc derivative to the corresponding 3-mcthylcyclohcx-2-cnol compound (see Figure 5-24). As this is a conventional organic reaction, the CIRX database should contain valuable information on how to syntbesi2e this product easily. 

INPADOC (International Patent Documentation Center) is the most comprehensive tttbliographic database of scientific and technological patent documents in the world. The stock encompasses more than 26 miUion patent documents, more than 59 miUion legal status data, and about 10 million patent famihes (January, 2003). The database contains more than 35 milhon patent citations from 71 patent-issuing organizations (European Patent Office, World Intellectual Property Organization (WlPO)) and is updated weekly with about 40 000 new citations. 

The abbreviation QSAR stands for quantitative structure-activity relationships. QSPR means quantitative structure-property relationships. As the properties of an organic compound usually cannot be predicted directly from its molecular structure, an indirect approach Is used to overcome this problem. In the first step numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical methods and artificial neural network models are used to predict the property or activity of interest, based on these descriptors or a suitable subset. A typical QSAR/QSPR study comprises the following steps structure entry or start from an existing structure database), descriptor calculation, descriptor selection, model building, model validation. 

More elaborate scheme.s can he envisaged. Thus, a. self-organizing neural network as obtained by the classification of a set of chemical reactions as outlined in Section 3,5 can be interfaced with the EROS system to select the reaction that acmaliy occurs from among various reaction alternatives. In this way, knowledge extracted from rcaetion databases can be interfaced with a reaction prediction system,... 

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