Exploring the Database

What can we now do with this small model data base We may, for instance, ask the system whether aconitine in the system tb has a hrf value of 45. To do this we enter at the system prompt - the question 

PROLOG now searches through the data base to find an entry which matches exactly our input. If it finds one, it returns yes on the screen (or true or something equivalent depending on the implementation). If, however, we ask 

Applications of the above type are rather trivial. We may, however, submit more sophisticated questions, as for example Is there a compound that in system ta has a hrf value of 25 . PROLOG uses the convention that a string beginning with a capital letter represents a variable. Initially a variable represents a yet undefined object. During the reasoning process an object might become associated with this variable, i.e. the variable becomes instantiated. Thus, if we ask 

PROLOG tries again to match the input statement with the entries in the data base. The variable Compound is instantiated to whatever object happens to occupy the respective position in the data base entry. If a match is found, the value assigned to the variable is put out. In our example the result (its presentation may again vary slightly with different implementations) will be 

There may be other solutions of the problem at hand. The system thus asks whether we are satisfied with the solution just given or whether it should try to find alternate solutions. In some implementations the system poses the respective question explicitly, awaiting a specified response (generally yes or no). Other systems just wait for the user to act. The convention in these systems is often that a carriage return signals accepting the solution, where as input of a indicates that alternate solutions are to be found. In the present case, there are no other solutions. Thus, if we ask for more we get the answer no. Another interesting question may be 

In the above left screen capture, the Explore tab is selected. In this mode, we can explore the database like we would a hard disk using the Windows Explorer. To expand the tree, click the LEFT mouse button on the as shown  

Expand the CAPACITORS branch or the tree until you see the Electrolytic section  

Click on the text Electrolytic to display the available electrolytic capacitors  

You can scroll through the list if you like. Hundreds of capacitors are contained in this list. To select a part, click the LEFT mouse button on the line in the database you want to select. If the line is highlighted in green, then the part is correctly linked to a graphic symbol in one of the symbol libraries. If a symbol and PCB layout footprint are found, they will be displayed in separate windows  

A footprint is not displayed because the parts in the Digikey database do not have footprints associated with them. To place the selected part in your schematic, click the RIGHT mouse button on the highlighted part and then select Place Database Part  

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We would like to further explore the database, so press the z key to place a database part ... 

In the auxiliary memory condition, the subjects could refer at any stage in their solving process, to a note card on which they could read all the information they had decided to record when they explored the database. 

The system was built on a Microsoft Windows NT server platform, by a Microsoft Internet Information Server and browsed by a Microsoft hitemet Explorer. The database is a Microsoft SQL server but the information can be integrated into other legacy databases. The main program for displaying further applications is EQOS Administrator which has been developed by using Microsoft Visual Basic and e-commerce functionahty. 

Recent initiatives have been introduced, in particular for metabolomic experiments for plants such as Armet but also the Metabolomics Standard Initiative [FIE 07]. These initiatives help, among others, to share a certain number of data concerning the standards and thus facilitate by automation the aimotation of compounds. Much software today helps explore the databases to propose chemical structures. Most of the time they use the ID and 2D NMR data, such as the Chenomx, Amix (Bruker), Metabominer, rNMR or even BMRB software. 

HyperChem provides two forms of parameter sets — an ASCII text form and the database form consistent with dBASEIV and a large number of other database packages. Since the principal difficulty with using molecular mechanics is having or obtaining appropriate parameters, you may want to explore parameter sets as a database in the. dbf form. 

One element of database generation that is a key consideration is whether to expand the representative compounds to include alternative tautomers, protonated and deprotonated forms of the molecule, and also to enumerate stereochemistry fully if not specified in the input. Depending on the molecules in question and the options considered, these can lead to a 10-fold increase in the size of the database to be explored. However, such an expansion is necessary if methods are used that are sensitive to such chemical precision (e.g., docking). For 3D similarity searching, it is sometimes more efficient to consider various modifications to the query, leading to multiple searches against a smaller database. 

Frequently one will find new citations (not found in the database just completed) in the next bibliographical database that one searches. Are these new citations unique to this second bibliographic database, or were they present in the first database but your search did not find them To build quality control into your search process, return to the first bibliographical database and search for the newfound citations using an author or a title-word search statement. If you do find the newfound citations in the first database, explore the citations index/key words. You may discover additional appropriate search terms or procedures to improve your search. 

Since 1997, staff members of the West Virginia Geological and Economic Survey (WVGES) have been building a geochemical database for the State s bedrock units to be used for the dual purposes of mineral exploration and environmental quality assessment. The results of these efforts were first released to the public in 2001 when the number of sample analyses in the database reached approximately five hundred (McDowell,... 

Munro et al. (1996) explored the relationship between chemical structure and toxicities through the compilation of a large reference database consisting of 613 chemical substances tested for a variety of noncarcinogenic toxicological endpoints in rodents and rabbits in oral toxicity tests, including subchronic, chronic, reproductive, and developmental toxicity. For many of the substances, more... 

The applications of the database to the exploration of in vitro-in vivo relationships (referred to as bioinformatics applications in Fig. 1) have been the focus of the last sections of this chapter. Applications of BioPrint include predicting biological and pharmaceutical properties of existing... 

The concepts of molecular similarity (1-3) and molecular diversity (4,5) play important roles in modern approaches to computer-aided molecular design. Molecular similarity provides the simplest, and most widely used, method for virtual screening and underlies the use of clustering methods on chemical databases. Molecular diversity analysis provides a range of tools for exploring the extent to which a set of molecules spans structural space, and underlies many approaches to compound selection and to the design of combinatorial libraries. Many different similarity and diversity methods have been described in the literature, and new methods continue to appear. This raises the question of how one can compare different methods, so as to identify the most appropriate method(s) for some particular application this chapter provides an overview of the ways in which this can be carried out, illustrating such comparisons by,... 

This set of working hypotheses can be used to guide explorations on how to make structure changes in other chemical species to influence water solubility. However, its reliability has to be tested, especially when extrapolated to materials that are far from these simple organic compounds. The verification also enlarges the database, and may lead to revisions of the hypothesis. 

Consequently, the aims in this chapter are to critically examine the available literature on the flavonoid composition of foods and to establish a food flavonoid database, which can be continually expanded as more information becomes available. By using predetermined selection criteria to ensure critical assessment of data quality, the intention is to provide researchers with an improved resource for use in studies exploring the relationships between flavonoid intake and health as well as highlighting important food groups where flavonoid content data are currently lacking. 

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