Empirical structure-property relations

The method begins with collecting all available information on substances xi, X2. xi. that are similar to the substance in question x°. These should be the substances that have the same basic structure as x°, but with some small structure differences, such as the number of repeated units, substitution of atoms by other atoms or small groups, rearrangement of the positions of substitution, rearrangement of the skeleton of the molecule, etcetera. The next move would be to subdivide the set S into structure-based subsets Sa, Sb,. .. and to observe the differences among their structures and their property values. One then proposes empirical structure-property relations for instance the members of family Sa tend to have higher values of y than members of family Sb, and within a family S, the property y may be positively 

The smell of musk is important in perfumes and cosmetics, and is obtained from the glands of small animals. There are several types of synthetic musk the aromatic musk is built on dinitrobenzene the steroid musk is built around cholesterol as four staggered fused rings and the macrocyclic musk is built on a 15-member carbon ring. Aside from the ability to elicit a pungent sensation in the nostrils and an emotional response of masculinity, we have not found the common structure responsible for these sensations. 

An empirical structure-property relation is based on observations in a set of fragmentary and incomplete data, instead of being based on a well-established theory of wide applicability and tested extensively. It is a tentative hypothesis designed to explain 

---

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. 

We give here a number of examples of empirical structure-property relations, and how they can be used to predict the effects of structure modifications and ambient conditions on properties. 

A summary of this small roster of empirical structure-property relations on water solubility shows ... 

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. 

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. 

Whatever the development of knowledge in the fields of chemical analysis and structure-property relationships, the characterization by determination of conventional properties of usage and other values related empirically to properties of usage will remain mandatory and unavoidable, as a minimum because it is required with regard to specifications. 

As the first commercial NMR instruments became available, a significant part of the empirical knowledge related to the structure and reactivity of organic compounds was under close scrutiny. Model compounds that could be used to test certain concepts or effects were subject to spectroscopic techniques and a framework for interpreting spectra based on structural properties began to develop. 

The most important step in a structure-property correlation is to discover the set of relevant structural parameters that control the property, as well as any related and readily available property parameters. The identification of these parameters may be suggested by theoretical understanding or by empirical observations of experimental measurements. There are some generally useful methods to discover the independent variables [x,] for a specific property in a set of molecules, such as the boiling points of the normal paraffins. 

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. 

Quality control or quality assurance tests are already common throughout the industry. Many of these tests are physical measurements that have been derived empirically. Rather than relating to the fundamental physical properties of foods, they are designed to detect deviation from a standard sample already defined to be of acceptable quality. While useful, they should not be confused with the measurements necessary to understand how components interact with each other to form the complex composite structure required in most foods. 

The industrial point of view of catalyst characterization is different. Here the main interest is to optimize or produce an active, selective, stable and mechanically robust catalyst. In order to accomplish this, tools are needed which identify those structural properties that discriminate efficient from less efficient catalysts. In principle, all spectroscopic information that helps to achieve this is welcome. Empirical relations between the factors that govern catalyst composition, particle size and shape, and pore dimensions on one side, and catalytic performance on the other can be extremely useful in developing catalysts. 

On examining the wealth of data provided by the m.OnO.m compounds, a simple empirical rule emerged relating the molecular structure to the occurrence of smectic behaviour specifically, if a symmetric dimer is to exhibit smectic properties then the terminal chain length must be greater than half the spacer length. A molecular interpretation of this empirical rule is discussed in Sect. 4.2. 

---