Quantitative structure-property relations QSPR

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. 

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>. 

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. 

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. 

Among the computational methods available, QSARs, or more general, quantitative structure-property relationships (QSPR) have been widely used not only in drug design and environmental chemistry but also in food-related studies. QSPR studies are grounded in the concept that a property (e.g., biological activity, reactivity, toxicity, volatility, etc.) depends on the molecular structure and that is possible to find a mathematical or quantitative relationship between that property and a suitable molecular representation (e.g., some combination of descriptors). 

There raises the idea that the atomic number has to be related, in principle, with all physical and chemical properties an atomic stmcture carries, or in a more phenomenological order, it appears as an effect or as a consequence of a certain existential elemental property. From this remark until the endeavor of viewing Z as the atomic activity/property that may be cast in terms of a plethora of structural indices is just a step and this is to be unveil in this communication, while testing one particular quantitative structure-property relationship (QSPR) for certain element leads, in fact, with testing the elemental periodicity of the Periodic System along a given period (Putz et al., 2011). 

The fundamental issue of elemental periodicity is here addresses through quantitative-structure-property-relationship (QSPR) by assuming the atomic number as the atomic activity/property to be correlated with structural indicators, among which those relating with outermost orbitals, electronegativity, chemical hardness, ionization potential and electronic... 

In the following paragraphs, some application examples will be presented, starting with a short introduction to COSMO-RS (Section 9.2), followed by solubility predictions in pure and mixed solvents (Section 9.3). A modification using several reference solubilities is shown in Section 9.4 whereas Section 9.5 is about quantitative structure-property relationship (QSPR) models of the melting point and the enthalpy of fusion. The final Sections 9.6 and 9.7 deal with COSMO-RS-based coformer selection for cocrystal screening and the related issue of solvent selection to avoid solvate formation. 

It is necessary here to consider the type of research which these methods may be used for. Historically, techniques for building models, both physical and mathematical, to relate biologicsd properties to chemical structure have been developed in pharmaceutical and agrochemical research. Many of the examples used in this text are derived from these fields of work. There is no reason, however, why any sort of property which depends on chemical structure should not be modelled in this way. This might be termed quantitative structure-property relationships (QSPR) rather than QSAR where A stands for activity. Such models are beginning to be reported recent examples include applications in the design of dyestuffs, cosmetics, egg-white substitutes, artificial sweeteners, cheese-making, and prepared food products. I have tried to incorporate some of these applications to illustrate the methods, as well as the more traditional examples of QSAR. 

In these cases, i.e. when a direct comparison with experimental observables is not possible, the results of Molecular Dynamics simulations can be used to provide the numerical representation of structure (codified by stmctural descriptors) to be related with the experimental properties of interest through mathematical models. This implies a shift from empirical composition-property relationships to computational structure-property relationships, thus acquiring an immense practical importance in the development of predictive and interpretative models [16]. This approach, called Quantitative Structure-Property Relationships (QSPR), is well known and extensively applied in the area of drug discovery, and chemical toxicology modeling. However, its application in the field of material design is only recently being explored [17-19]. 

Closely related to multivariate calibration are the applications of multivariate methods for investigations of structure-property or stmcture-activity relationships (see Quantitative Structure-Activity Relationships in Drug Design and Quantitative Structure-Property Relationships (QSPR)). 

One of the most extensively studied applications of this method focuses on the prediction of C NMR spectra (see Quantitative Structure-Property Relationships (QSPR)). The objective is the development of linear parametric equations that relate a set of descriptors to C NMR chemical shifts. The equations take the form ... 

Fig. 3.48 A general workflow of quantitative structure-activity relation-ship/quantitative structure-property relationship (QSAR/ QSPR) modelling...

In the cases of organic, inorganic, and organometaflic compounds, as well as for various polymers the quantitative structure—property/activity relationships (QSPRs/QSARs) approaches represent efficient and available tools one can use in order to predict numerical data related to an endpoint of unknown substances. In order to accomplish it and develop QSPR/QSAR model such approaches require... 

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