The analysis of network structure from the viewpoint of computer theory requires an introduction to some background information, which will be provided here. We will introduce the data involved for modeling metabolic pathways and the KEGG pathway database in particular. Furthermore, a basic introduction to graphs as used in computer science will be provided. [Pg.1815]

Some basic concepts and definitions of statistics, chemometrics, algebra, graph theory, similarity/diversity analysis, which are fundamental tools in the development and application of molecular descriptors, are also discussed in the book in some detail. More attention was paid to information content, multivariate correlation, model complexity, variable selection, applicability domain, and parameters for model quality estimation, as these are the characteristic components of modern QSAR/QSPR modeling. [Pg.1243]

An alternative stream came from the valence bond (VB) theory. Ovchinnikov judged the ground-state spin for the alternant diradicals by half the difference between the number of starred and unstarred ir-sites, i.e., S = (n -n)l2 [72]. It is the simplest way to predict the spin preference of ground states just on the basis of the molecular graph theory, and in many cases its results are parallel to those obtained from the NBMO analysis and from the sophisticated MO or DFT (density functional theory) calculations. However, this simple VB rule cannot be applied to the non-alternate diradicals. The exact solutions of semi-empirical VB, Hubbard, and PPP models shed light on the nature of spin correlation [37, 73-77]. [Pg.242]

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