Qualitative structure-property relationship

The simplest case of structure-property relationships are qualitative rules of thumb. For example, the statement that branched polymers are generally more biodegradable than straight-chain polymers is a qualitative structure-property relationship. 

Bagley (1983) and Blanshard and Lillford (1987) dealing with qualitative structure-property relationships in meats, plant foods and baked products]. Evidence is now accumulating that structure does play a key role in controlling many other attributes important in foods beyond their basic physical or engineering properties. For example, structure is critical in texture perception (Hutchings and Lillford 1988), flavor release (Taylor 2002) and the bioavailability of some nutrients (Aguilera 2005). 

M.-H. Whangbo received his Ph.D. in chemistry in 1974 working under the direction of V. H. Smith, Jr. and S. Wolfe at Queen s University, Canada. After postdoctoral studies with S. Wolfe at Queen s University and R. Hoffmann at Cornell University, he joined the faculty of North Carolina State University in 1978. Throughout his scientihc career, he has explored qualitative structure-property relationships in discrete molecules, extended solids and surfaces on the basis of formal theory developments and electronic structure calculations. 

Structure-property relationships are qualitative or quantitative empirically defined relationships between molecular structure and observed properties. In some cases, this may seem to duplicate statistical mechanical or quantum mechanical results. However, structure-property relationships need not be based on any rigorous theoretical principles. 

In the second half of the nineteenth century the structural theory of organic chemistry was developed. It led to the concept that chemical, physical and biological properties of all kinds must vary with structural change. The earliest structure-property relationships (SPR) were qualitative. With the development of methods of quantitative measurement of these properties data accumulated. Attempts were then made to develop quantitative models of the structural dependence of these properties. These methods for the quantitative description of structural effects will now be described. 

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. 

Although both the ab-initio derivative method and the semi-empirical sum-over-states approach have been used with some success to predict qualitative trends, they are not sufficiently developed to have predictive capabilities for structure-property relationship. Clearly, there is a need to develop semi-empirical theoretical methods which can reliably be used to predict, with cost-effectiveness and with reasonable computational time, molecular and polymeric structures with enhanced optical nonlinearity. 

A trend to more complex problems and the availability of automated instruments are novel aspects of modern scientific research. When complex problems are investigated it is usually necessary to characterize an object (e.g. a sample, a reaction, a fact) not only by one parameter (measurement, feature) but by several parameters. The aim of the investigation is often to obtain a better insight into the treated problem, rather in a qualitative than in a quantitative manner. In chemistry such demands for an exploratory data analysis frequently arise in connection with analytical work on complex samples, e.g. environmental samples and also in the field of structure-property-relationships. With modern, sometimes called intelligent, instruments a great amount of data can easily be obtained from samples. The bottle-neck in this work is the data interpretation. 

Inherent in the concept of molecular structure is the notion that properties of all kinds, chemical, physical and biological, must vary with structural change. At first the structure -property relationships (SPR) reported were qualitative. As quantitative measurements of... 

They indicated [211] that, while this equation reflects the state-of-the-art, its major limitations prevent one from obtaining quantitative structure-property relationships based on it. It does not involve the structural parameters of the material it takes no account of the relaxation (prefracture) state and it provides no way to describe any stepwise transitions, discontinuities, and abrupt qualitative changes upon the interaction of a material with its environment. They then devised a new method based on their autooscillation model of the solid state, and showed the promise of this method by using a heat-resistant polyimide as their example. 

The problem of emulsification and stabilization of dispersions is still to be addressed. At present, only mostly qualitative statements are available. E.g., good emulsifying properties are implied for the reported use of polysoaps in emulsion polymerization [376] and for the stabilization of latexes [50, 214], Good emulsifying properties are reported as well for natural polysoaps [79, 80], Oligomeric polysoaps are efficient emulsifiers for liquid hydrocarbons [82]. High dispersing efficiency for alumina particles is claimed for some polymerized surfactants [377, 378]. But structure-property relationships are still to be... 

When we progress from the foregoing qualitative discussion of structure-property relationships to the quantitative specification of mechanical properties, we enter a jungle that has been only partially explored. The most convenient point of departure into this large and complex subject is provided by the topic of "linear viscoelasticity." Linear viscoelasticity represents a relatively simple extension of classical (small-strain) theory of elasticity. In situations where linear viscoelasticity applies, the mechanical properties can be determined from a few experiments and can be specified in any of several equivalent formulations (11). 

Thus, in all these ways, the structure-property relationships in solid polymers do carry over qualitatively into their foams as well, but often with considerable quantitative modification. In terms of practical properties of maximum importance, other structural features of the foam, particularly gas and density, may be much more Important than the particular polymer itself. 

In understanding structure-property relationships in a variety of molecules and solids, the concepts of perturbation, orbital interaction, orbital mixing and orbital occupation are widely employed. Theoretical bases of these qualitative concepts are briefly surveyed. 

Early attempts to map the properties of an unknown receptor (or any other ligand binding site) started from qualitative structure-activity relationships [878], from MO calculations of preferred conformations of ligands [879] and from the interpretation of multiparameter Hansch equations (e.g. Figure 48) [28]. 

This chapter is somehow related to the goals of dimensional analysis. However, it does not deal with dimensional analysis in the closer sense. Structure - property relationships find use in a wide field of science, in particular in connection with molecular modeling. Most simply, structure - property relationships are qualitative thumb rules. For example, an experienced scientist can predict if a molecule would have a dipole moment or not. Even the organic chemist has an idea on the boiling point, if you present him a structural molecular formula. 

Recently studies in our laboratories as well as those of Prasad, Reinhardt and coworkers on structure-property relationships of NLO materials has led us to be able to make some qualitative predictions on design goals for new ladder monomers. Four substitution patterns of election-donating (D) and accepting (A) substituents are accessible from our synthetic scheme for ladder monomers, as illustrated below in Figure 4. 

As previously mentioned, the acronym QSAR stands for the quantitative structure-activity relationship. However, there may be some ambiguity associated with the attribute quantitative. It does not necessarily follows that results expressed or having numerical representation are necessarily quantitative. Qualitative results can equally be numerically represented. Strictly speaking, we define and view QSAR models as quantitative only when the numerically expressed models allow meaningful interpretation of the numerical results obtained for the structure-activity relationship within the basic concepts of the particular model. This means that the physicochemical models should allow quantitative interpretation of the numerical physicochemical descriptors used and that the structure-mathematical models should allow quantitative interpretation of the numerical structure-mathematical descriptors used. We will use the symbol qsar and QSAR as the abbreviation for qualitative structure-activity relationship. Such are the relationships that are non-numerical and the relationships that may be numerical but the variables used are interrelated and thus do not allow unique interpretation of the MRA equations. Because all molecular descriptors hitherto used in QSAR, whether they are based on physicochemical properties, quantum mechanical calculations, or molecular graphs, are all interrelated, it follows that all such hitherto reported results, without further elaboration, remain essentially qualitative, being qsar rather than QSAR. 

L. Linati, G. Lusvardi, G. Malavasi, L. Menabue, M.C. Menziani, P. MustareUi, U. Segre, Qualitative and quantitative structure-property relationships (QSPR) analysis of multicomponent potential bioglasses. J. Phys. Chem. B 109,4989-4998 (2005)... 

SAR work can be classified into two categories QSAR (quantitative structure-activity relationships) and qSAR (qualitative structure-activity relationships). In QSAR analysis, biological activity is quantitatively expressed as a function of physico-chemical properties of molecules. QSAR involves modeling a continuous activity for quantitative prediction of the activity of new compounds. qSAR aims to separate the compounds into a number of discrete types, such as active and inactive or good and bad. It involves modeling a discrete activity for qualitative prediction of the activity of new compounds. 

---