QSAR and QSPR,

The MEP at the molecular surface has been used for many QSAR and QSPR applications. Quantum mechanically calculated MEPs are more detailed and accurate at the important areas of the surface than those derived from net atomic charges and are therefore usually preferable [Ij. However, any of the techniques based on MEPs calculated from net atomic charges can be used for full quantum mechanical calculations, and vice versa. The best-known descriptors based on the statistics of the MEP at the molecular surface are those introduced by Murray and Politzer [44]. These were originally formulated for DFT calculations using an isodensity surface. They have also been used very extensively with semi-empirical MO techniques and solvent-accessible surfaces [1, 2]. The charged polar surface area (CPSA) descriptors proposed by Stanton and Jurs [45] are also based on charges derived from semi-empirical MO calculations. 

To know what QSAR and QSPR are, and the steps in QSAR/QSPR. 

Topological Indices and Related Descriptors in QSAR and QSPR J. Devillers, A. T. 

Ivanduc, O., Balahan, A. T. The graph description of chemical structures. In Topological Indices and Related Descriptors in QSAR and QSPR, Devillers,). 

Devillers, J., Balaban, A. T. Topological indices and Related Descriptors In QSAR and QSPR, Gordon Breach, Amsterdam, 1999. 

Breneman, C., and M. Martinov. 1996. The Use of the Electrostatic Potential Fields in QSAR and QSPR. In Molecular Electrostatic Potentials Concepts and Applications, edited by J. S. Murray and K. D. Sen. Elsevier, Amsterdam. 

Accordingly, sorption has received a tremendous amount of attention and any method or modeling technique which can reliably predict the sorption of a solute will be of great importance to scientists, environmental engineers, and decision makers (references herein and in Chaps. 2 and 3). The present chapter is an attempt to introduce an advanced modeling approach which combines the physical and chemical properties of pollutants, quantitative structure-activity, and structure-property relationships (i. e., QSARs and QSPRs, respectively), and the multicomponent joint toxic effect in order to predict the sorption/desorp-tion coefficients, and to determine the bioavailable fraction and the action of various organic pollutants at the aqueous-solid phase interface. 

In summary, the QSAR and QSPR approaches, as well as their modeling techniques, are important and a basic need for environmental planning and engineering management. Molecular connectivity indices (MCIs) are a sensitive property for many organic pollutants. Such MCIs can be used to predict the partitioning of pollutants at interfaces as will be seen in Sect. 3. 

This section represents different case studies to explain how physical and chemical properties, QSAR and QSPR approaches, and multicomponent toxic effect models can be used to predict the mobility and bioavailability of organic pollutants at aqueous-solid phase interfaces. Such interdisciplinary approaches are applied here to two groups of toxic and carcinogenic compounds. 

Hall LH, Kier LB (1999) In Devillers J, Balaban AT (eds) Topological indices and related descriptors in QSAR and QSPR. Gordon and Breach, Reading, UK... 

In the present work, we will use a relatively low level of theory to derive 32 weakly correlated molecular descriptors, each based on the subdivision and classification of the molecular surface area according to three fundamental properties contribution to ClogP, molar refractivity, and atomic partial charge. The resulting collection will be shown to have applicability in QSAR, QSPR, and compound classification. Moreover, the derived 32 descriptors linearly encode most of the information of a collection of traditional mathematical descriptors used in QSAR and QSPR. 

By dividing the problem this way, we translate it from an abstract problem in catalysis to one of relating one multi dimensional space to another. This is still an abstract problem, but the advantage is that we can now quantify the relationship between spaces B and C using QSAR and QSPR models. Note that space B contains molecular descriptor values, rather than structures. These values, however, are directly related to the structures (8). 

Building predictive QSAR and QSPR models is a cost-effective way to estimate biological activities, physicochemical properties such as partition coefficients and solubility, and more complicated pharmaceutical endpoints such as metabolic stability and volume of distribution. It seems to be reasonable to assume that structurally similar molecules should behave similarly. That is, similar molecules should have similar biological activities and physicochemical properties. This is the (Q)SAR/(Q)SPR hypothesis. Qualitatively, both molecular interactions and molecular properties are determined by, and therefore are functions of, molecular structures. Or... 

There is a long history of efforts to find simple and interpretable /i and fi functions for various activities and properties (29, 30). The quest for predictive QSAR models started with Hammett s pioneer work to correlate molecular structures with chemical reactivities (30-32). However, the widespread applications of modern predictive QSAR and QSPR actually started with the seminal work of Hansch and coworkers on pesticides (29, 33, 34) and the developments of various powerful analysis tools, such as PLS (partial least squares) and neural networks, for multivariate analysis have fueled these widespread applications. Nowadays, numerous publications on guidelines, workflows, and... 

J.L. Faulon et al., The signature molecular descriptor. 1. Using extended valence sequences in QSAR and QSPR studies. J. Chem. Inf. Comput. Sci. 43, 707-720 (2003)... 

In a broader context, LFER and similar approaches are subsets of correlation analyses. Exner defines correlation analysis as a mathematical treatment starting from experimental data and seeking empirical relationships which can subsequently be interpreted theoretically. Although certainly not restricted to chemistry, correlation analysis has been developed extensively in physical organic chemistry. In addition to LFER, LSER, QSAR, and QSPR involve empirical models and, hence, fall in the category of correlation analysis. 

Erom an operational standpoint, the LFER, LSER, QSAR, and QSPR approaches can be quite similar, with distinctions based on their applications. QSAR is usually applied to biological properties, especially those important to pharmacology and toxicology. QSPR usually dwells on physicochemical properties in general. LSER focuses on solute-solvent systems. For organizational purposes, we like to view LSER and some applications of QSAR and QSPR (along with related methods) as subsets of LFER. Each approach typically uses some form of regression analysis (statistics) to help find a mathematical relationship between a property and a set of descriptors. 

From another viewpoint, LFER methods tend to be model based. Model-based methods employ sets of descriptors that often (1) model classical chemical concepts, (2) are small in number, and (3) use simple regression analyses. For example, the Flammett equation involving the logarithm of the rate constant as a linear function of the substituent constant, a (mentioned earlier), is model based. Similarly, some QSAR and QSPR studies may be viewed in this manner, and so they are included as LFER subsets in this chapter. 

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