Chemical Connections INDEX

The WLN was applied to indexing the Chemical Structure Index (CSI) at the Institute for Scientific Information (ISI) [13] and the Ituiex Chemicus Registry System (ICRS) as well as the Crossbow System of Imperial Chemical Industries (ICl). With the introduction of connection tables in the Chemical Abstracts Service (CAS) in 1965 and the advent of molecular editors in the 1970s, which directly produced connection tables, the WLN lost its importance. 

Correlation methods discussed include basic mathematical and numerical techniques, and approaches based on reference substances, empirical equations, nomographs, group contributions, linear solvation energy relationships, molecular connectivity indexes, and graph theory. Chemical data correlation foundations in classical, molecular, and statistical thermodynamics are introduced. 

As computing capabiUty has improved, the need for automated methods of determining connectivity indexes, as well as group compositions and other stmctural parameters, for existing databases of chemical species has increased in importance. New naming techniques, such as SMILES, have been proposed which can be easily translated to these indexes and parameters by computer algorithms. Discussions of the more recent work in this area are available (281,282). SMILES has been used to input Contaminant stmctures into an expert system for aquatic toxicity prediction by generating LSER parameter values (243,258). 

The main characteristic of cluster-type indices is that all bonds are connected to the common, central atom (star-type structure). The third-order cluster molecular connectivity index (3yc) is the first, simplest member of the cluster-type indices where three bonds are joined to the common central atom [102-104, 111-113,152-154,166,167,269]. The simplest chemical structure it refers to is the non-hydrogen part of ferf-butane. This index is then calculated using Eq. (43) ... 

A further extension of this approach was done by Kier and Hall8> so as to provide different values of the connectivity index for molecules depicted by one and the same graph, but differing by the chemical nature of atoms as well as by the presence of single, double or triple bonds. The valency of the atom i (vertex degree), Vj, is replaced by the atom connectivity ... 

Two basic quantities are tire atomic simple connectivity index 8 and the atomic valence connectivity index 5. These values are tabulated in Bicerano s book (p. 17) for 11 chemical elements, namely C, N, O, F, Si, P, S, Se, Cl, Br, tnd I. Values of 8 and S are also reported for various hybridizations (sp, sp, etc.). 8 is equal to the number of nonhydrogen atoms to which a given atom is bonded. 8" is calculated through ... 

More recently, Brennan et al. (1997) compared five methods for estimating KAW, namely the vapor pressure/solubility ratio, the group or bond contribution method, linear solvation energy methods, and molecular connectivity. The authors compared the methods by application to a common set of 150 chemicals and concluded that the Meylan and Howard (1991) bond contribution method and the molecular connectivity index method of Nirma-lakhandan and Speece (1988) are comparably accurate, having standard deviations of, 0.29 and 0.34 log units, respectively. 

Koch, R. (1983) Molecular connectivity index for assessing ecotoxicological behaviour of organic chemicals. Toxicol. Environ. Chem. 6, 87-96. 

Dowdy, D.L., McKone, T.E. (1997) Predicting plant uptake of organic chemicals from soil or air using octanol/water and octanol/air partition ratios and a molecular connectivity index. Environ. Toxicol. Chem. 16, 2448-2456. 

The aforementioned macroscopic physical constants of solvents have usually been determined experimentally. However, various attempts have been made to calculate bulk properties of Hquids from pure theory. By means of quantum chemical methods, it is possible to calculate some thermodynamic properties e.g. molar heat capacities and viscosities) of simple molecular Hquids without specific solvent/solvent interactions [207]. A quantitative structure-property relationship treatment of normal boiling points, using the so-called CODESS A technique i.e. comprehensive descriptors for structural and statistical analysis), leads to a four-parameter equation with physically significant molecular descriptors, allowing rather accurate predictions of the normal boiling points of structurally diverse organic liquids [208]. Based solely on the molecular structure of solvent molecules, a non-empirical solvent polarity index, called the first-order valence molecular connectivity index, has been proposed [137]. These purely calculated solvent polarity parameters correlate fairly well with some corresponding physical properties of the solvents [137]. 

Go el and Madan [117] investigated the 8AR of the antiulcer activity of these compounds with Wiener s topological index (Wiener number of chemical graph, W(G)) [118] and the first-order molecular connectivity index ( x) [119] using a typical classification procedure. In the case of Wiener s... 

These are truly structural descriptors because they are based only on the two-dimensional representation of a chemical structure. The most widely known descriptors are those that were originally proposed by Randic (173) and extensively developed by Kier and Hall (27). The strength of this approach is that the required information is embedded in the hydrogen-suppressed framework and thus no experimental measurements are needed to define molecular connectivity indices. For each bond the Ck term is calculated. The summation of these terms then leads to the derivation of X, the molecular connectivity index for the molecule. 

Caporossi, G., Gutman, I. and Hansen, P. (1999). Variable Neighborhood Search for Extremal Graphs. IV Chemical Trees with Extremal Connectivity Index. Computers Chem., 23, 469-477. 

Dowdy, D.L., McKone, T.E. and Hsieh, D.P. (1996). Prediction of Chemical Biotransfer of Organic Chemicals from Cattle Diet into Beef and Milk Using the Molecular Connectivity Index. Environ.ScLTechnoL, 30, 984-989. 

This index was designed as an extension of the Randic connectivity index to take into account the relative size of heteroatoms in a H-depleted molecular graph. It is based on the Madan vertex degree derived from the chemical adjacency matrix [Goel and Madan, 1995] ... 

Chemical adjacency matrices based on relative atomic masses were used to calculate the —> atomic molecular connectivity index, Zagreb topochemical indices, and the superadjacency topochemical index, all defined in terms of the Madan chemical degree 5 ", which is the row sum of the atomic weight-weighted adjacency matrix. 

Araujo, O. and De La Pena, J.A. (1998) Some bounds for the connectivity index of a chemical graph. 

Mu, L. and Peng, C. (2004) Novel connectivity index of edge valence and its applications. Journal of Chemical Industrial Engineering (China) 55, 531-540. 

Three major approaches to the prediction of aqueous solubility of organic chemicals using Quantitative Structure Activity Relationship (QSAR) techniques arc reviewed. The rationale behind six QSAR models derived from these three approaches, and the quality of their fit to the experimental data are summarized. Their utility and predictive ability are examined and compared on a common basis. Three of the models employed octanol-water partition coefficient as the primary descriptor, while two others used the solvatochromic parameters. The sixth model utilized a combination of connectivity indexes and a modified polarizability parameter. Considering the case of usage, predictive ability, and the range of applicability, the model derived from the connectivity- polarizability approach appears to have greater utility value. 

Pathway 6 requires the calculation of an intermediate, x (the molecular connectivity index), which is a topological index. The regression equations that link x to Kow (pathway 7) cover only a relatively small number of monofunctional-group chemical classes. 

HBi is zero for all hydrocarbons and, therefore, was deleted from analyses of BP. Twelve of the TIs were deleted for the analysis of the 140 hydrocarbons as well. These indexes included the third- and fourth-order chain connectivity indexes, which were zero for all chemicals, the fourth- and sixth-order bond and valence corrected cluster connectivity indexes, which were perfectly correlated with the simple cluster connectivity indexes (r = 1.0), and and which were perfectly correlated with 7 for hydrocarbons. 

Twelve of the TIs were dropped from the study of the log data set. The third-and fourth-order chain connectivity indexes were zero for all chemicals and the fifth-order chain connectivity index was nonzero for only one chemical. The sixth-order cluster connectivity indexes were nonzero for only one compound as well. Therefore, 89 indexes were used for the variable clustering. 

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