Structural property relationship

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. 

A challenging task in material science as well as in pharmaceutical research is to custom tailor a compound s properties. George S. Hammond stated that the most fundamental and lasting objective of synthesis is not production of new compounds, but production of properties (Norris Award Lecture, 1968). The molecular structure of an organic or inorganic compound determines its properties. Nevertheless, methods for the direct prediction of a compound s properties based on its molecular structure are usually not available (Figure 8-1). Therefore, the establishment of Quantitative Structure-Property Relationships (QSPRs) and Quantitative Structure-Activity Relationships (QSARs) uses an indirect approach in order to tackle this problem. In the first step, numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical and artificial neural network models are used to predict the property or activity of interest based on these descriptors or a suitable subset. 

Two approaches to quantify/fQ, i.e., to establish a quantitative relationship between the structural features of a compoimd and its properties, are described in this section quantitative structure-property relationships (QSPR) and linear free energy relationships (LFER) cf. Section 3.4.2.2). The LFER approach is important for historical reasons because it contributed the first attempt to predict the property of a compound from an analysis of its structure. LFERs can be established only for congeneric series of compounds, i.e., sets of compounds that share the same skeleton and only have variations in the substituents attached to this skeleton. As examples of a QSPR approach, currently available methods for the prediction of the octanol/water partition coefficient, log P, and of aqueous solubility, log S, of organic compoimds are described in Section 10.1.4 and Section 10.15, respectively. 

P. C. Juts, Quantitative structure-property relationships, in Encyclopedia of Computational Chemistry, Volume 4, P. v. R. Schleyer, N. L. Allinger, T. Qaik, J. Gasteiger, P. A. KoUman, H. F. Schaefer III and P.R. Schreiner (Eds.), John Wiley Sons, Chichester, 1998, pp. 2320-2330. 

Rogers D and A J Hopfinger 1994. Application of Genetic Function Approximation to Quantitatir Structure-Activity Relationships and Quantitative Structure-Property Relationships. Journal Chemical Information and Computer Science 34 854-866. 

An example of using one predicted property to predict another is predicting the adsorption of chemicals in soil. This is usually done by first predicting an octanol water partition coelficient and then using an equation that relates this to soil adsorption. This type of property-property relationship is most reliable for monofunctional compounds. Structure-property relationships, and to a lesser extent group additivity methods, are more reliable for multifunctional compounds than this type of relationship. 

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. 

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. 

When structure-property relationships are mentioned in the current literature, it usually implies a quantitative mathematical relationship. Such relationships are most often derived by using curve-fitting software to find the linear combination of molecular properties that best predicts the property for a set of known compounds. This prediction equation can be used for either the interpolation or extrapolation of test set results. Interpolation is usually more accurate than extrapolation. 

When the property being described is a physical property, such as the boiling point, this is referred to as a quantitative structure-property relationship (QSPR). When the property being described is a type of biological activity, such as drug activity, this is referred to as a quantitative structure-activity relationship (QSAR). Our discussion will first address QSPR. All the points covered in the QSPR section are also applicable to QSAR, which is discussed next. 

PW91 (Perdew, Wang 1991) a gradient corrected DFT method QCI (quadratic conhguration interaction) a correlated ah initio method QMC (quantum Monte Carlo) an explicitly correlated ah initio method QM/MM a technique in which orbital-based calculations and molecular mechanics calculations are combined into one calculation QSAR (quantitative structure-activity relationship) a technique for computing chemical properties, particularly as applied to biological activity QSPR (quantitative structure-property relationship) a technique for computing chemical properties... 

D. M. Parkin, in C. J. McHargue, R. Kossowsky, and W. O. Hofer, eds.. Structure—Property Relationships in Surface-Modified Ceramics Kluwer Academic Publishers, Dordrecht, 1989, p. 47. 

T. Hioki and co-workers, ia C. McHargue and co-workers, eds.. Structural—Property Relationships in Surface Modified Ceramics, Kluwer Academic Pubhshers, Dordrecht, the Netherlands, 1989, p. 303. 

Quantitative Structure-Property Relationships. A useful way to predict physical property data has become available, based only on a knowledge of molecular stmcture, that seems to work well for pyridine compounds. Such a prediction can be used to estimate real physical properties of pyridines without having to synthesize and purify the substance, and then measure the physical property. 

Structure—Property Relationships The modem approach to the development of new elastomers is to satisfy specific appHcation requirements. AcryUc elastomers are very powerhil in this respect, because they can be tailor-made to meet certain performance requirements. Even though the stmcture—property studies are proprietary knowledge of each acryUc elastomer manufacturer, some significant information can be found in the Hterature (18,41). Figure 3a shows the predicted according to GCT, and the volume swell in reference duid, ASTM No. 3 oil (42), related to each monomer composition. Figure 3b shows thermal aging resistance of acryHc elastomers as a function of backbone monomer composition. 

The structure/property relationships in materials subjected to shock-wave deformation is physically very difficult to conduct and complex to interpret due to the dynamic nature of the shock process and the very short time of the test. Due to these imposed constraints, most real-time shock-process measurements are limited to studying the interactions of the transmitted waves arrival at the free surface. To augment these in situ wave-profile measurements, shock-recovery techniques were developed in the late 1950s to assess experimentally the residual effects of shock-wave compression on materials. The object of soft-recovery experiments is to examine the terminal structure/property relationships of a material that has been subjected to a known uniaxial shock history, then returned to an ambient pressure... 

To illustrate the effect of radial release interactions on the structure/ property relationships in shock-loaded materials, experiments were conducted on copper shock loaded using several shock-recovery designs that yielded differences in es but all having been subjected to a 10 GPa, 1 fis pulse duration, shock process [13]. Compression specimens were sectioned from these soft recovery samples to measure the reload yield behavior, and examined in the transmission electron microscope (TEM) to study the substructure evolution. The substructure and yield strength of the bulk shock-loaded copper samples were found to depend on the amount of e, in the shock-recovered sample at a constant peak pressure and pulse duration. In Fig. 6.8 the quasi-static reload yield strength of the 10 GPa shock-loaded copper is observed to increase with increasing residual sample strain. 

D Rogers, AJ Hopflnger. Application of genetic function approximation to quantitative strac-ture-activity relationships and quantitative structure-property relationships. J Chem Inf Comput Sci 34(4) 854-866, 1994. 

In order to fully appreciate the widespread application that molecular modeling can find in beginning organic chemistry, it is important to appreciate the fundamental relationship between molecular structure and chemical, physical and biological properties. So-called structure-property relationships are explored in nearly every college chemistry course, whether introductory or advanced. Students are first taught about the structures of molecules, and are then taught how to relate structure to molecular properties. 

Applications of neural networks are becoming more diverse in chemistry [31-40]. Some typical applications include predicting chemical reactivity, acid strength in oxides, protein structure determination, quantitative structure property relationship (QSPR), fluid property relationships, classification of molecular spectra, group contribution, spectroscopy analysis, etc. The results reported in these areas are very encouraging and are demonstrative of the wide spectrum of applications and interest in this area. 

MW and MWD are very significant parameters in determining the end use performance of polymers. However, difficulty arises in ascertaining the structural properties relationship, especially for the crystalline polymers, due to the interdependent variables, i.e., crystallinity, orientation, crystal structure, processing conditions, etc., which are influenced by MW and MWD of the material. The presence of chain branches and their distribution in PE cause further complications in establishing this correlation. 

The structure-property relationship of graft copolymers based on an elastomeric backbone poly(ethyl acry-late)-g-polystyrene was studied by Peiffer and Rabeony [321. The copolymer was prepared by the free radical polymerization technique and, it was found that the improvement in properties depends upon factors such as the number of grafts/chain, graft molecular weight, etc. It was shown that mutually grafted copolymers produce a variety of compatibilized ternary component blends. 

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