Structural classification methods

All current structural classification methods are based on the same scheme Protein structures are first divided into discrete, globular domains, which are then classified at the levels of (1) class, (2) folds, (3) superfa-milies, and (4) families. The differences among existing schemes come from the methods that define the domains and the procedures that classify. After reviewing the terms that define a classification, the three main protein structure classifications available, SCOP, CATH, and the DALI Domain Dictionary (DDD), will be described. Links to these databases and related services are listed in Table 7. 

Exploratory analysis of spectral data by PCA, PLS, cluster analysis, or Kohonen mapping tries to get an insight into the spectral data structure and into hidden factors, as well as to find clusters of similar spectra that can be interpreted in terms of similar chemical structures. Classification methods, such as LDA. PLS, SIMCA, KNN classification, and neural networks, have been used to generate spectral classifiers for an automatic recognition of structural properties from spectral data. The multivariate methods mostly used for spectra prediction (mainly NMR. rarely IR) are neural networks. Table 6 contains a summary of recent works in this field (see Infrared Data Correlations with Chemical Structure). 

CLASSIFICATION METHODS In image processing, it often very interesting to built classes from the data structure. This technique can be partitioned into two categories ... 

This structure encoding method has been applied both for the classification of a data set comprising 31 corticosteroids, for which affinity data were available in the literature, binding to the corticosteroid-binding globulin (CBG) receptor, and for the simulation of infrared spectra [28, 29). 

W. Werther, H. Lohninger, F. Stand and K. Vermuza, Classification of mass spectra. A comparison of yes/no classification methods for the recognition of simple structural properties. Chemom. Intell. Lab. Syst., 22 (1994) 63-67. 

In the chemical classification method, colorants are grouped according to certain common chemical structural features. The most important... 

It should be noted that the above classification system of technetium cluster compounds is not the only possible one. In section 4 another classification is described, which is based on thermal stability and the mechanism of thermal decomposition. Section 2.2 is concerned with the classification based on methods of synthesizing cluster compounds. The classifications based on specific properties of clusters do not at all belittle the advantages of the basic structural classification they broaden the field of application of the latter, because for a better understanding and explanation of any chemical, physico-chemical and physical properties it is necessary to deal directly or indirectly with the molecular and/or electronic structures of the clusters. 

Although the development of a SIMCA model can be rather cumbersome, because it involves the development and optimization of J PCA models, the SIMCA method has several distinct advantages over other classification methods. First, it can be more robust in cases where the different classes involve discretely different analytical responses, or where the class responses are not linearly separable. Second, the treatment of each class separately allows SIMCA to better handle cases where the within-class variance structure is... 

Crude oils are classified chemically according to the structures of tire larger molecules in the mixture. Classification methods use combinations of the words paraffinic, naphthenic, aromatic, and asphaltic. For instance, crude oil which contains a predominance of paraffinic molecules will yield very fine lubricating oils from the gas-oil fraction and paraffin wax from the residuum. Oh the other hand, if the larger molecules are aromatic and asphaltic, the heavier fractions of the crude oil are useful for pitch, roofing compounds, paving asphalts, and other such applications. 

Terms such as paraffinic, naphthenic, naphthenic-aromatic, and aromatic-asphaltic are used in the several classification methods which have been proposed. These terms obviously relate to the molecular structure of the chemical species most prominent in the crude oil mixture. However, such classification is made difficult because the large molecules usually consist of condensed aromatic and naphthenic rings with paraffinic side chains. The characteristic properties of the molecules depend on the proportions of these structures. 

One classification method treats a large molecule as aromatic if it has a single benzene ring regardless of the other content. Another method considers the fraction of each molecule that is aromatic, naphthenic, or paraffinic. Obviously, in either case the analysis procedure is tedious. A third classification method simply measures the specific gravities of several fractions separated by distillation and attempts to relate chemical structure to specific gravity. 

Most structural materials are susceptible to a wide range of defects. Any flaw alters the behavior of a structure, even if only minutely. The larger the flaw the more it reduces the useful properties of the material. One of the challenges in modem materials engineering is defect reduction. Defect reduction involves defect detection, defect source determination and mechanisms and defect elimination. There is no single method of detect review that can fully characterize every defect each defect classification method has its own strengths. 

The surface extended X-ray absorption fine structure (SEXAFS) method can use either an electron or an ion detection signal (Koningsberger and Prins, 1988). The classification of analytical techniques may be considered in terms of incident and emitted radiation, resolution, and sensitivity, according to Table 4.7, which lists eight of the many possible techniques (Briggs and Seah, 1990 Buckley, 1981 Watts, 1990). Many of the surface analysis techniques were introduced into many laboratories over the years of 1968 to 1970. This resulted from the maturing of clean vacuum systems which could achieve pressures, down to 10"8 Pa. At these low pressures, it is possible to obtain and maintain atomically clean surfaces. 

Figure 13.11 Overview diagram of the NCTR Four-Phase approach for priority setting. In Phase I, chemicals with molecular weight < 94 or > 1000 or containing no ring structure will be rejected. In Phase II, three approaches (structural alerts, pharmacophores, and classification methods) that include a total of 11 models are used to make a qualitative activity prediction. In Phase III, a 3D QSAR/CoMFA model is used to make a more accurate quantitative activity prediction. In Phase IV, an expert system is expected to make a decision on priority setting based on a set of rules. Different phases are hierarchical different methods within each phase are complementary.

Principal component analysis is central to many of the more popular multivariate data analysis methods in chemistry. For example, a classification method based on principal component analysis called SIMCA [69, 70] is by the far the most popular method for describing the class structure of a data set. In SIMCA (soft independent modeling by class analogy), a separate principal component analysis is performed on each class in the data set, and a sufficient number of principal components are retained to account for most of the variation within each class. The number of principal components retained for each class is usually determined directly from the data by a method called cross validation [71] and is often different for each class model. 

The ADP moiety is a very flexible structure and could assume a variety of conformations in the binding site. However, in practice there are only a small number of bound conformations that are actually observed. The architecture of the site, conserved by evolution, appears to restrict the conformations found. The approach described in Section 2 is to apply classification methods to the ligand conformations and then to hunt for structural and functional correlations derived from the site which are associated with the observed ligand conformations. 

The dimensionality of chemical structure space exceeds that of known biological functional space. The dimensionality of biological functional space has increased in recent years due to the discovery of a multitude of genes, largely from the Human Genome Project. This chapter, however, will focus on chemical diversity rather than functional diversity. Quantification of chemical diversity involves two areas first, the predefmition of a chemical space, accomplished by selection of a diversity metric and a compound representation (i.e., molecular descriptors) and second, a rational subset selection, or classification, method dependent on efficient dimensionality reduction. Here, we describe these methods, prerequisites for a definition... 

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