Statistically base interaction indice

Statistically Based Interaction Indices Derived from... 

Murray, J. S., T. Brinck, P. Lane, K. Paulsen, and P. Politzer. 1994. Statistically-Based Interaction Indices Derived From Molecular Surface Electrostatic Potentials A General Interaction Properties Function (GIPF). J. Mol. Struct. (Theochem) 307, 55. 

Statistically Based Interaction Indices Derived from Molecular Surface Electrostatic Potentials A General Interaction Properties Function (GIPF). 

Murray, J.S., Brinck, T, Lane, P Paulsen, K. and Politzer, P. (1994) Statistically-based interaction indices derived from molecular surface electrostatic potentials a general interaction properties function (GIPF)./. Mol. Struct. (Theochem), 307, 55-64. 

In the preceding sections of this chapter, we have focused on the use of extrema in the electrostatic potential, i.e. Vmin. Vs,min and Vs,max. for th interpretation and prediction of site-specific molecular interactions. However, additional information about a molecule s ability to interact with other molecules can be obtained by an analysis of the overall pattern of the electrostatic potential on the molecular surface. Politzer and co-workers have in recent years developed a number of statistically-based interaction indices that are defined in terms of the entire surface electrostatic potential [100-102]. They have further shown... 

It is not within the scope of this article to present a full review of all the applications of statistically-based interaction indices. We will instead give a short presentation of the most important indices followed by an example of the use of these indices in the analysis of a solvation process the partitioning of solutes between octanol and water. 

We believe that relationships like eq. 20 that combines the use of global statistically based interaction indices with local interaction indices, such as the Vmin, can be very useful for stud dng solvation processes. In particular, for... 

The macroscopic property of interest, e.g., heat of vaporization, is represented in terms of some subset of the computed quantities on the right side of Eq. (3.7). The latter are measures of various aspects of a molecule s interactive behavior, with all but surface area being defined in terms of the electrostatic potential computed on the molecular surface. Vs max and Fs min, the most positive and most negative values of V(r) on the surface, are site-specific they indicate the tendencies and most favorable locations for nucleophilic and electrophilic interactions. In contrast, II, a ot and v are statistically-based global quantities, which are defined in terms of the entire molecular surface. II is a measure of local polarity, °fot indicates the degree of variability of the potential on the surface, and v is a measure of the electrostatic balance between the positive and negative regions of V(r) (Murray et al. 1994 Murray and Politzer 1994). 

The retention times of analytes are controlled by the concentration(s) of the organic solvent(s) in the mobile phase. If a relatively small entropic contribution to the retention is neglected, theoretical considerations based either on the model of interaction indices [58], on the solubility parameter theory [51,52] or on the molecular statistical theory [57], lead to the derivation of a quadratic equation for the dependence of the logarithm of the retention factor of a solute. A, on the concentration of organic solvent. aqueous-organic mobile phase ... 

A three-state RIS model is derived for POE, based upon ab initio electronic structure analyses of model molecules DME and DEE. It is demonstrated that the low energy of the tg gI conformation of DME, resulting from strong O -H attractions, as indicated by the ab initio studies, necessitates the inclusion of third-order interactions in the RIS model. This is realized by adopting 9x9 statistical weight matrices. 

The statistical thermodynamic approach, along lines already indicated, has been more tractable and suggestive. Models have been based on the Fowler-Guggenheim treatment of localized monolayers, in which account is taken of energy terms arising from interaction between point defects in nearest neighbor... 

Another problem that has been tackled by multivariate statistical methods is the characterization of the solvation capability of organic solvents based on empirical parameters of solvent polarity (see Chapter 7). Since such empirical parameters of solvent polarity are derived from carefully selected, strongly solvent-dependent reference processes, they are molecular-microscopic parameters. The polarity of solvents thus defined cannot be described by macroscopic, bulk solvent characteristics such as relative permittivities, refractive indices, etc., or functions thereof. For the quantitative correlation of solvent-dependent processes with solvent polarities, a large variety of empirical parameters of solvent polarity have been introduced (see Chapter 7). While some solvent polarity parameters are defined to describe an individual, more specific solute/solvent interaetion, others do not separate specific solute/solvent interactions and are referred to as general solvent polarity scales. Consequently, single- and multi-parameter correlation equations have been developed for the description of all kinds of solvent effects, and the question arises as to how many empirical parameters are really necessary for the correlation analysis of solvent-dependent processes such as chemical equilibria, reaction rates, or absorption spectra. 

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