Molecular Structure Databases

Other related coding languages are derived from enhancements of SMILES (XSMILES, SMARTS, SMIRKS, STRAPS, CHUCKLES, CHORTLES, CHARTS [22]). Each of them was designed to represent special molecular structures or to allow particular applications (polymers, mixtures, reactions, or database-handling). 

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. 

There are now extensive databases of molecular structures and properties. There are some research efforts, such as drug design, in which it is desirable to hnd all molecules that are very similai to a molecule which has the desired property. Thus, there are now techniques for searching large databases of structures to hnd compounds with the highest molecular similarity. This results in hnding a collection of known structures that are most similar to a specihc compound. 

Another technique employs a database search. The calculation starts with a molecular structure and searches a database of known spectra to find those with the most similar molecular structure. The known spectra are then used to derive parameters for inclusion in a group additivity calculation. This can be a fairly sophisticated technique incorporating weight factors to account for how closely the known molecule conforms to typical values for the component functional groups. The use of a large database of compounds can make this a very accurate technique. It also ensures that liquid, rather than gas-phase, spectra are being predicted. 

As already stated above, the database has been developed using ISIS software. The program operation is very simple, and about 30 min to learn the particular commands of this structure-searching program. ISIS provides both storage and retrieval of chemical structures. It is also possible to store text and numeric data into database entries. Because molecular structures are searchable in many ways, ISIS software is an excellent tool for exploiting data, and not simply archiving it. 

ISIS databases are hierarchical, so CHIRBASE was designed to incorporate about 60 data fields on several levels of detail (the main fields are listed in Table 4-2). The first level contains the molecular structure of the sample combined to the molecular structure of the CSP, producing a unique location or entry for a specific sample-CSP couple. Consequently, in the current version of CHIRBASE, which contains 40 000 entries, one entry corresponds to the separation of one sample on one CSP and contains in different sublevels a compilation of all the references and the various analytical conditions available for this separation. 

All the sections must be completed by the user and then submitted to a single administrator for addition to the database. Upon completion of the form, the user has the option of making a check submission, which processes the data and performs error checks as normal, but displays the verdict on screen for the user rather than sending the data to the administrator. A variety of errors are checked, including missing data and inconsistent data, invalid molecular structures or numeric data outside the normal range. When the user is satisfied with the form data, they can be submitted to the administrator via the exporf button. Upon submission, the data are stored... 

CHIRBASE provides integrated responses from single questions, as well as from combinatorial questions constructed on the basis of any specific query corresponding to one or several field(s) occurring in the database. With the molecular structure of a sample in hand, the search can be conducted interactively from the query menu form. 

Fig. 8.11 The molecular structures of 42 and 43. Compound 43 was identified by searching a multiconformational database for molecules that were similar in shape to 42.

In the case of being successful in calculating multiple conformations by using time- or ensemble-averaged MD restraints the solved molecular structures are presented as 3D models and can be deposited in an electronic structure database (17). Finally, it is recommended to provide an accurate explanation of the procedures used for the structure elucidation because the application of different methods (NMR, DG, MD, SA, Monte-Carlo calculations. X-ray crystallography) may result in varying conformational models which do not implicitly display the real state of a molecule. This aspect should be always kept in mind when dealing with structure determination methods. 

Fig. 14. The molecular structure of l,3-bis(diphenylhydroxysilyl)-2,2-dimethyl-4,4-diphenylcyclodisilazane, showing the orientation of the ortho -CH groups of one of the aromatic rings with respect to the SiO groups. Other hydrogen atoms have been omitted for clarity. Drawn using coordinates taken from the Cambridge Crystallographic Database.

Molecular Gas Phase Documentation (MOGADOC). Chemieinformationssysteme, Universitat Ulm, Germany. Electronic database of molecular structures in the gas phase. WWW.uni-ulm.de/strudo/mogadoc/. 

The QCRNA database is viewable and searchable with a web browser on the internet and it is also contained as a MySQL database that is easily incorporated with parameter optimization software to allow for the rapid development of specific reaction parameters. Molecular structures can be viewed with the JMOL [47, 48] or MOLDEN [49, 50] programs as viewers for chemical MIME types. If the web browser is JAVA-enabled, then the JMOL software will automatically load as a web applet. Both programs allow the structure to be manipulated, i.e., rotated, scaled, and translated, and allow for measurement of internal coordinates, e.g., bond lengths, angles, and dihedral angles. Similarly, animations of the vibrational frequencies are available and can be viewed with either program. 

The role of an artificial neural network is to discover the relationships that link patterns of input data to associated output data. Suppose that a database contains information on the structure of many potential drug molecules (the input) and their effectiveness in treating some specific disease (the output). Since the clinical value of a drug must in some way be related to its molecular structure, correlations certainly exist between structure and effectiveness, but those relationships may be very subtle and deeply buried. 

We have already met one tool that can be used to investigate the links that exist among data items. When the features of a pattern, such as the infrared absorption spectrum of a sample, and information about the class to which it belongs, such as the presence in the molecule of a particular functional group, are known, feedforward neural networks can create a computational model that allows the class to be predicted from the spectrum. These networks might be effective tools to predict suitable protective glove material from a knowledge of molecular structure, but they cannot be used if the classes to which samples in the database are unknown because, in that case, a conventional neural network cannot be trained. 

Visualization software allows the user to display molecular structures imported from databases or other software programs. Chime, RasMol, and Protein Explorer programs are available at the websites listed below for Windows operating PCs and Macintosh PowerPC computers. 

As shown above, CN and ON are important properties of diesel and gasoline fuels, respectively. Although a set of large databases are available, cetane and octane values of many individual compounds are still missing. Therefore, the prediction tools we have developed are very useful in correlating the molecular structures with properties of fuels. In this section, the predicted CN and ON values are implemented to construct the most effective catalytic strategies to optimize CN for diesel and ON for gasoline. 

In contrast to the NRTL-SAC model, the UNIFAC model developed by Fredenslund et. al. [29] divides each molecule into a set of functional groups that interact with each other on a binaiy basis and whose interactions are combined together to describe the global liquid phase interaction between molecules. Because the segments in UNIFAC are based on functional groups it is possible to model a system provided that all of the molecular structures are known. The problem with pharmaceutical sized molecules is that existing UNIFAC parameter tables do not contain many of the group interaction parameters that are necessary, and even when they do, the interactions are fitted to a database of chemicals that are much smaller and simpler than pharmaceuticals, and typically fail to represent them adequately. 

To demonstrate the use of binary substructure descriptors and Tanimoto indices for cluster analysis of chemical structures we consider the 20 standard amino acids (Figure 6.3) and characterize each molecular structure by eight binary variables describing presence/absence of eight substructures (Figure 6.4). Note that in most practical applications—for instance, evaluation of results from searches in structure databases—more diverse molecular structures have to be handled and usually several hundred different substructures are considered. Table 6.1 contains the binary substructure descriptors (variables) with value 0 if the substructure is absent and 1 if the substructure is present in the amino acid these numbers form the A-matrix. Binary substructure descriptors have been calculated by the software SubMat (Scsibrany and Varmuza 2004), which requires as input the molecular structures in one file and the substructures in another file, all structures are in Molfile format (Gasteiger and Engel 2003) output is an ASCII file with the binary descriptors. 

Martin, Y.C., Danaher, E.B., May, C.S., and Weininger, D. MENTHOR, a database system for the storage and retrieval of three-dimensional molecular structures and associated data searchable by sub structural, biologic, physical, or geometric properties./. Comput.-Aided Mol. Des. 1998, 1, 15-29. 

Consider a molecular structure, which is the most important unifying information model in chemistry. Molecular structures appear in knowledgebases that represent catalogs of commercially available chemicals, pharmacology of named drugs, natural sources of bioactive molecules, protein-ligand interactions, measured molecular bioactivities, metabolic pathways, abstracted research literature, databases of synthetic reactions, and so on. 

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