Linear solvation energy method

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. 

In the solvatochromic or linear solvation energy method, developed by Kamlet et al. [34] and Taft et al. [35], the solubility is predicted from molar volume, melting point and two parameters which express dipolarity/polar-izability and hydrogen bond basicity. It has been applied to predict the solubility of very diverse type of compounds. 

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. 

Kamlet M. J., Abboud J.-L., Abraham M. H. and Taft R. W. (1983) Linear Solvation Energy Relationships. 23. A Comprehensive Collection of the Solvatochromic Parameters, 7i, a, and ft, and Some Methods for Simplifying the Generalized Solvatochromic Equation, J. Org. Chem. 48, 2877-2887. 

Much effort has been devoted to the development of reliable calculation methods for the prediction of the retention behaviour of analyses with well-known chemical structure and physicochemical parameters. Calculations can facilitate the rapid optimization of the separation process, reducing the number of preliminary experiments required for optimization. It has been earlier recognized that only one physicochemical parameter is not sufficient for the prediction of the retention of analyte in any RP-HPLC system. One of the most popular multivariate models for the calculation of the retention parameters of analyte is the linear solvation energy relationship (LSER) ... 

Kamlet MJ, Abboud JLM, Abraham MH, Taft RW (1983) Linear solvation energy relationships. 23. A comprehensive collection of the solvatochromic parameters, jr, a, and /3, and some methods for simplifying the generalized solvatochromic equation. J Org Chem 48 2877-2887. 

A variety of different approaches to the prediction of toxicity have been developed under the sponsorship of the Predictive Toxicology Evalnation project of the National Institnte of Environmental Health Sciences. The widespread application of compnta-tional techniqnes to stndies in biology, chemistry, and environmental sciences has led to a qnest for important, characteristic molecnlar parameters that may be directly derived from these compntational methods. Theoretical linear solvation energy relationships combine compntational molecular orbital parameters with the linear solvation energy relationship of Kamlet and Taft to characterize, nnderstand, and predict biological, chemical, and physical properties of chemical componnds (Eamini and Wilson, 1997). 

The method of linear solvation energy (LSER), based on the Kamlet-Taft multiparameter scale (10) has been successfully exploited to study retention in LC. The LSER approach, when applied to phase-transfer processes, correlates a general solute property (SP), such as logarithmic capacity factor, with parameters of the solute and both the mobile and stationary phases ... 

A linear solvation energy relationship (LSER) method was developed by Abraham et al. (1994) using five "solvatochronic" parameters for 408 chemicals. This method is related to the LSER method described in Chapter 7. Obtaining the parameter values can be demanding and difficult, but is potentially powerful. 

Almlof M, Carlsson J, Aqvist J (2007) Improving the accuracy of the linear interaction energy method for solvation free energies. J Chem Theory Comput 3(6) 2162-2175... 

Another objective of this chapter is to explain how LFER fits in with respect to linear solvation energy relationships (LSER), quantitative structure-activity relationships (QSAR), and quantitative structure-property relationships (QSPR). Often, these methods are operationally quite similar. Their connection is addressed in the Background section. 

M. J. Kamlet, J. L. M. Abboud, M. H. Abraham, and R. W. Taft, Linear Solvation energy relationships. 23. A comprehensive collection of the solvatochromic para-menters, pi, alfa, and bets, and some methods for simphfying the generalized solvatochromic eqnation, J. Org. Chem. 48 (1983), 2877-2887. 

A rational strategy in identifying structural parameters appropriate for QSRR analysis should start from the accepted theories of chromatographic separations. These structural parameters obtained. should quantify the abilities of analytes to take part in the postulated intermolecular interactions which determine chromatographic. separations. Empirical or semi-empirical structural parameters of analytes based on the solvatochromic comparison method and on the linear solvation energy relationships (LSER) belong to that categoiy of structural descriptors. 19,40). 

Quantum-chemical descriptors are used in several QSAR approaches, such as, for example, theoretical linear solvation energy relationships (TLSERs), - Mezey 3D shape analysis, - GIPF approach, - CODESSA method, -> ADAPT approach. 

The term classical QSAR is often used to denote the - Hansch analysis, -> Free-Wilson analysis, -> Linear Free Energy Relationships (LFER) and -> Linear Solvation Energy Relationships (LSER), i.e. those SRC approaches developed between 1960 and 1980 that can be considered the beginning of the modern QSAR/QSPR methods. 

QSAR method based on the philosophy of the -> Linear Solvation Energy Relationships, whose empirically derived molecular descriptors are substituted hy descriptors defined in the framework of - computational chemistry [Famini et ai, 1991 Famini et ai, 1992 Famini and Wilson, 1994a]. The TLSER descriptors were developed with the aim of optimally correlating with LSER descriptors, thereby being as generally applicable to solute/solvent interactions as are the LSER descriptors. 

Molar volume can also be calculated by additive fragment methods [Elbro et al, 1991 Schotte, 1992] such as the fragment method of LeBas [Reid et al, 1988]. Moreover, it is frequently used as a measure of the - cavity term in - linear solvation energy relationships. 

There is no such clear-cut judgment about the statistical methods of modeling solubility. There are models as simple as the relationship between log Pand melting point (MP), established some time ago by Yalkowsky and coworkers, and the very complex linear solvation energy relationships (LSERs). The limitation of the simple Yalkowsky relationship is that it uses two variables, obtained with accuracy only by measurement, and thus the simple relationship turns out to be very complicated when calculated log P and MP are used. 

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