Quantitative structure-property relations

Physical properly estimation methods may be classified into six general areas (1) theory and empirical extension of theory, (2) corresponding states, (3) group contributions, (4) computational chemistry, (5) empirical and quantitative structure property relations (QSPR) correlations, and (6) molecular simulation. A quick overview of each class is given below to provide context for the methods and to define the general assumptions, accuracies, and limitations inherent in each. 

Mu, L. and Peng, C. (2007) Quantitative structure-property relations (QSPRs) for predicting standard absolute entropy, S 298, of inorganic compounds. MATCH Commun. Math. Comput. Chem., 57, 111-134. 

Toropov, A.A., Toropova, A.P., Muftahov, R.A., Ismailov, T. and Muftahov, A.G. (1994) Simulation of molecular systems by the ideal symmetry method for revealing quantitative structure-property relations. Russ.J. Rhys. Chem., 68, 577-579. 

Thiadiazole 1 and its derivatives were used as model compounds for the calculation of molecular parameters related to physical properties for their use in quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) studies <1999EJM41, 2003IJB2583, 2005JMT27>. 

Quantitative analysis of ESP is important for several reasons. The precise knowledge of the ESP is necessary to allow comparison of atomic potentials in different structures, analysis of composition (partial occupancy) and chemical bonding (crystal formation, structure property relations). 

Rogers, D. Hopfingee, A.J. Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships. J. Chem. Inf. Comput. Sci. 1994, 34, 854-866. Kubinyi, H. Variable selection in QSAR studies. 1. An evolutionary algorithm. Quantum Struct.-Act. Relat. 1994, 13, 285-294. 

When theoretical understanding is insufficient and quantitative correlations are not available, we can often make useful qualitative estimations by using fragmentary empirical structure-property relations. The principal tools are observations of associations and trends, which are often the only methods available in biological, health, safety, and environmental properties. 

In this relatively young field of research, the experimental and theoretical results lend themselves better to associations and trends analysis than to quantitative correlations and theoretical predictions. Empirical structure-property relations should be regarded as temporary props that can be useful, but should be modified if serious exceptions are found. They should be regarded as a stage in the evolution from ignorance to empiricism, and then into knowledge and reliable theory, and should be supplanted when better ideas arrive. 

Classes of Estimation Methods Table 1.1.1 summarizes the property estimation methods considered in this book. Quantitative property-property relationships (QPPRs) are defined as mathematical relationships that relate the query property to one or several properties. QPPRs are derived theoretically using physicochemical principles or empirically using experimental data and statistical techniques. By contrast, quantitative structure-property relationships (QSPRs) relate the molecular structure to numerical values indicating physicochemical properties. Since the molecular structure is an inherently qualitative attribute, structural information has first to be expressed as a numerical values, termed molecular descriptors or indicators before correlations can be evaluated. Molecular descriptors are derived from the compound structure (i.e., the molecular graph), using structural information, fundamental or empirical physicochemical constants and relationships, and stereochemcial principles. The molecular mass is an example of a molecular descriptor. It is derived from the molecular structure and the atomic masses of the atoms contained in the molecule. An important chemical principle involved in property estimation is structural similarity. The fundamental notion is that the property of a compound depends on its structure and that similar chemical stuctures (similarity appropriately defined) behave similarly in similar environments. 

Figures 2.20 and 2.21 show the significant difference between diffusion in liquids and in rubbery and glassy polymers. A great deal of work has been performed over the last two decades to achieve a quantitative link between the structure of polymers and their permeation properties. No such quantitative structure-property relationship is at hand or even in sight. What has been achieved is a set of semiempirical rules that allow the permeation properties of related families of polymers to be correlated based on small changes in their chemical structures. The correlating tool most generally used is the polymer s fractional free volume v/ (cm3/cm3), usually defined as...

Recently, Riviere and Brooks (2007) published a method to improve the prediction of dermal absorption of compounds dosed in complex chemical mixtures. The method predicts dermal absorption or penetration of topically applied compounds by developing quantitative structure-property relationship (QSPR) models based on linear free energy relations (LFERs). The QSPR equations are used to describe individual compound penetration based on the molecular descriptors for the compound, and these are modified by a mixture factor (MF), which accounts for the physical-chemical properties of the vehicle and mixture components. Principal components analysis is used to calculate the MF based on percentage composition of the vehicle and mixture components and physical-chemical properties. 

In some cases standardisation (or closely related scaling) is an essential first step in data analysis. In case study 2, each type of chromatographic measurement is on a different scale. For example, the N values may exceed 10 000, whereas k rarely exceeds 2. If these two types of information were not standardised, PCA will be dominated primarily by changes in N, hence all analysis of case study 2 in this chapter involves preprocessing via standardisation. Standardisation is also useful in areas such as quantitative structure-property relationships, where many different pieces of information are measured on very different scales, such as bond lengths and dipoles. 

The elucidation of the dependence of various chemical and physical properties of substances on molecular structure can be considered as one of the main goals of theoretical chemistry. Although an immense knowledge has accumulated in this field, a fairly limited number of direct, causal and quantitative (or at least semiquantitative) structure-property relations have been discovered so far. The main reason for this is the enormous complexity of the quantum-chemical calculations, by means of which the contemporary theoretical chemists try to describe and predict the behaviour of molecules. During such calculations the insight into the actual connection between the input (e.g. molecular structure) and output (e.g. certain molecular properties) is usually completely lost. 

Other Examples of the Use of Principal Properties Characterization by principal properties has been reported for classes of compounds in applications other than organic synthesis Aminoacids, where principal properties have been used for quantitative structure-activity relations (QSAR) of peptides [64], Environmentally hazardous chemicals, for toxicity studies on homogeneous subgroups [65]. Eluents for chromatography, where principal properties of solvent mixtures have been used for optimization of chromatographic separations in HPLC and TLC [66],... 

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