QSPR method

It is important to realize that many important processes, such as retention times in a given chromatographic column, are not just a simple aspect of a molecule. These are actually statistical averages of all possible interactions of that molecule and another. These sorts of processes can only be modeled on a molecular level by obtaining many results and then using a statistical distribution of those results. In some cases, group additivities or QSPR methods may be substituted. 

QSPR methods have yielded the most accurate results. Most often, they use large expansions of parameters obtainable from semiempirical calculations along with other less computationally intensive properties. This is often the method of choice for small molecules. 

The development of group additivity methods is very similar to the development of a QSPR method. Group additivity methods can be useful for properties that are additive by nature, such as the molecular volume. For most properties, QSPR is superior to group additivity techniques. 

Unfortunately, there is not at a present a computational method for predicting the UL temperature index. There is a QSPR method for predicting... 

Air direct photolysis t,/2 = 1.17 h predicted by QSPR method in atmospheric aerosol (Chen et al. 2001). Surface water photolysis t,/2 = 0.35 h near surface water, 40°N midday, midsummer and photosensitized oxygenation t,/2 = 1.5 h at near surface water, 40°N, midday, midsummer (Zepp Schlotzhauer 1979). photolysis t,/2 = 0.18 h in aqueous solution when irradiated with a 500 W medium pressure mercury lamp (Chen et al. 1996). 

Recently, ILs similar to those presented in this section have been under intense investigation. The quantitative structure-property relationship (QSPR) method to the analysis of values obtained in different laboratories... 

A selection of these physical constants for pyrroles, furans and thiophenes is included in Table 32 of Chapter 2.4 and trends are discussed there (Section 2.4.4.1.1), together with data for five-membered rings containing two or more heteroatoms. A computer-assisted QSPR method has been proposed for predicting the normal boiling point for new furans, tetrahydrofurans, and thiophenes (91JCI301, 98JCI28). 

Bencze, L., Toth, G., and Kurdi, R., Predicting the environmental hazards of xenobiotics by QSPR methods I. Ethylene oxide, Hungarian J. Ind. Chem., 28,187-193, 2000. 

The examples presented here help us to address the question of whether to use a model-based (small sets of simply interpreted descriptors) or nonmodel-based QSAR/QSPR method (large sets of descriptors). The model-based equations (which includes LFERs) can be used to fairly readily predict the result of changing molecular structure on a property. This is because these equations can often be easily interpreted from a chemical viewpoint. The nonmodel-based equations are frequently not so easily interpreted... 

QSPR - methods correlating calculated structural descriptors with pA ... 

The term classical QSAR is often used to denote the - Hansch analysis, -> Free-Wilson analysis, -> Linear Free Energy Relationships (LFER) and -> Linear Solvation Energy Relationships (LSER), i.e. those SRC approaches developed between 1960 and 1980 that can be considered the beginning of the modern QSAR/QSPR methods. 

Empirical QSPR Correlations In quantitative structure property relationship (QSPR) methods, physical properties are correlated with molecular descriptors that characterize the molecular and electronic structure of the molecule. Large amounts of experimental data are used to statistically determine the most significant descriptors to be used in the correlation and their contributions. The resultant correlations are simple to apply if the descriptors are available. Descriptors must generally be generated by the user with computational chemistry software, although the DIPPR 801 database now contains a table of molecular descriptors for most of the compounds in it. QSPR methods are often very accurate for specific families of compounds for which the correlation was developed, but extrapolation problems are even more of an issue than with GC methods. 

Zhokova, N.I., Palyulin, V.A., Baskin, I.I., Zefirov, A. N. and Zefirov, N.S. (2007) Fragment descriptors in the QSPR method their use for calculating the enthalpies of vaporization of organic substances. Russ. J. Phys. Chem., 81, 9-12. 

Another QSPR method, which was developed by Askadskii [34], can also often be used to predict Tm for polymers. 

An attempt has been made to use the quantitative structure-property relationship (QSPR) method to correlate and predict the melting points of organic salts based on the quaternary ammonium cation [5], Moderate correlations were found for a set of 75 tetraalkylammonium bromides (see Figure 1), and for a set of 34 (n-hydroxyalkyl)trialkylammonium bromides. Descriptors used in the correlations were analyzed to determine which structural features led to lower melting points (e.g., asymmetry in the ions - see below). However, this technique cannot, as yet, extend to the prediction of melting points for salts that are either chemically or topologically dissimilar to those used in defining the QSPR. 

A number of free and commercial packages for modeling pK, s and other properties are available. Many of these include some dependence on QSPR methods. One example is the SPARC program (Spare On-Line Calculator. 

All of them mostly employ any of the machine learning-based quantity structure-activity relationship (QSAR)/QSPR methods for property prediction. 

Structure-activity relationship (SAR) and, more generally, stracture-property relationship (SPR) analysis are integral to the rational drag design cycle. Quantitative (QSAR, QSPR) methods assume that biological activity is correlated with chemical structures or properties and that as a consequence activity can be modelled as a function of calculable physiochemical attributes. Such a model for activity prediction could then be used, for instance, to screen candidate lead compounds or to suggest directions for new lead molecules. 

An experience acquired during developments of QSAR/QSPR methods applied to various nanomaterials allows establishing useful principles applied to obtaining optimal descriptors. Few key rules are given below ... 

The first two assumptions are implicit, although often not stated, in many other QSAR/QSPR methods. The third assumption may be accounted for to some extent by the deliberate inclusion of several examples of each substituent. 

More recent extensions of QSPR methods to more complex phenomena, such as the viscosity of dispersions (246) or diffusion of gases through polymers (247), have been developed. These works should he consulted for more penetrating hih-liographies of literature in this field. 

QSPR methods are based on the hypothesis that changes in the structure are reflected in changes in observed macroscopic properties of materials. The basic strategy of QSPR analysis is to find optimum statistical correlations, which can then be used for... 

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