Quantitative structure-physical property relationships

QSPR quantitative structure-physical property relationship... 

Quantitative structure-physical property relationships (QSPR). There are two types of physical properties we must consider ground state properties and properties which depend on the difference in energy between the ground state and an excited state. Examples of the former are bond lengths, bond angles and dipole moments. The latter include infrared, ultraviolet, nuclear magnetic resonance and other types of spectra, ionization potentials and electron affinities. 

In the optimisation process, we realized that the concept of bioisosterism and the available data for quantitative structure-physical property relationships were potentially a great help to pursue the optimisation effectively. But at the same time I should mention that even if comprehensive lists of bioisosteres and physicochemical parameters of... 

Quantitative structure-physical property relationships (QSPR). These involve infrared, ultraviolet, nuclear magnetic resonance and other types of spectra, bond lengths and bond angles, dipole moments, ionization potentials and electron affinities. 

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. 

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. 

The final physical properties of thermoset polymers depend primarily on the network structure that is developed during cure. Development of improved thermosets has been hampered by the lack of quantitative relationships between polymer variables and final physical properties. The development of a mathematical relationship between formulation and final cure properties is a formidable task requiring detailed characterization of the polymer components, an understanding of the cure chemistry and a model of the cure kinetics, determination of cure process variables (air temperature, heat transfer etc.), a relationship between cure chemistry and network structure, and the existence of a network structure parameter that correlates with physical properties. The lack of availability of easy-to-use network structure models which are applicable to the complex crosslinking systems typical of "real-world" thermosets makes it difficult to develop such correlations. 

In a study by Andersson et al. [30], the possibilities to use quantitative structure-activity relationship (QSAR) models to predict physical chemical and ecotoxico-logical properties of approximately 200 different plastic additives have been assessed. Physical chemical properties were predicted with the U.S. Environmental Protection Agency Estimation Program Interface (EPI) Suite, Version 3.20. Aquatic ecotoxicity data were calculated by QSAR models in the Toxicity Estimation Software Tool (T.E.S.T.), version 3.3, from U.S. Environmental Protection Agency, as described by Rahmberg et al. [31]. To evaluate the applicability of the QSAR-based characterization factors, they were compared to experiment-based characterization factors for the same substances taken from the USEtox organics database [32], This was done for 39 plastic additives for which experiment-based characterization factors were already available. 

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>. 

Because of the large number of chemicals of actual and potential concern, the difficulties and cost of experimental determinations, and scientific interest in elucidating the fundamental molecular determinants of physical-chemical properties, considerable effort has been devoted to generating quantitative structure-property relationships (QSPRs). This concept of structure-property relationships or structure-activity relationships (QSARs) is based on observations of linear free-energy relationships, and usually takes the form of a plot or regression of the property of interest as a function of an appropriate molecular descriptor which can be calculated using only a knowledge of molecular structure or a readily accessible molecular property. 

There is a continuing effort to extend the long-established concept of quantitative-structure-activity-relationships (QSARs) to quantitative-structure-property relationships (QSPRs) to compute all relevant environmental physical-chemical properties (such as aqueous solubility, vapor pressure, octanol-water partition coefficient, Henry s law constant, bioconcentration factor (BCF), sorption coefficient and environmental reaction rate constants from molecular structure). 

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. 

Methods have been presented, with examples, for obtaining quantitative structure-property relationships for alternating conjugated and cross-conjugated dienes and polyenes, and for adjacent dienes and polyenes. The examples include chemical reactivities, chemical properties and physical properties. A method of estimating electrical effect substituent constants for dienyl and polyenyl substituents has been described. The nature of these substituents has been discussed, but unfortunately the discussion is very largely based on estimated values. A full understanding of structural effects on dienyl and polyenyl systems awaits much further experimental study. It would be particularly useful to have more chemical reactivity studies on their substituent effects, and it would be especially helpful if chemical reactivity studies on the transmission of electrical effects in adjacent multiply doubly bonded systems were available. Only further experimental work will show how valid our estimates and predictions are. 

Domain Where Physics, Chemistry, Biology, and Technology Meet (see above) p. 11. The paper, Use of quantitative structure-property relationships in predicting the Krafft point of anionic surfactants by M. Jalali-Heravi and E. Konouz, Internet Electronic Journal of Molecular Design, 2002, 1, 410, has a nice introduction and useful references. It can be downloaded at http //www.biochempress.com/av01 0410.html. 

Physical Properties, Transport and Degradation of Environmental Fate and Exposure Assessments, Quantitative Structure-Activity Relationships in Environmental Sciences, VII, Chapter 13, SETAC Press, USA. 

This example belongs to the area quantitative structure-property relationships (QSPR) in which chemical-physical properties of chemical compounds are modeled by chemical structure data—mostly built by multivariate calibration methods as described in this chapter und using molecular descriptors (Todeschini and Consonni... 

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. 

The concept of the similarity of molecules has important ramifications for physical, chemical, and biological systems. Grunwald (7) has recently pointed out the constraints of molecular similarity on linear free energy relations and observed that Their accuracy depends upon the quality of the molecular similarity. The use of quantitative structure-activity relationships (2-6) is based on the assumption that similar molecules have similar properties. Herein we present a general and rigorous definition of molecular structural similarity. Previous research in this field has usually been concerned with sequence comparisons of macromolecules, primarily proteins and nucleic acids (7-9). In addition, there have appeared a number of ad hoc definitions of molecular similarity (10-15), many of which are subsumed in the present work. Difficulties associated with attempting to obtain precise numerical indices for qualitative molecular structural concepts have already been extensively discussed in the literature and will not be reviewed here. 

A more common use of informatics for data analysis is the development of (quantitative) structure-property relationships (QSPR) for the prediction of materials properties and thus ultimately the design of polymers. Quantitative structure-property relationships are multivariate statistical correlations between the property of a polymer and a number of variables, which are either physical properties themselves or descriptors, which hold information about a polymer in a more abstract way. The simplest QSPR models are usually linear regression-type models but complex neural networks and numerous other machine-learning techniques have also been used. 

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