Structure QSAR

With the development of accurate computational methods for generating 3D conformations of chemical structures, QSAR approaches that employ 3D descriptors have been developed to address the problems of 2D QSAR techniques, e.g., their inability to distinguish stereoisomers. The examples of 3D QSAR include molecular shape analysis (MSA) [34], distance geometry [35,36], and Voronoi techniques [37]. 

Classical chemo metric methods, such as the Quantitative Structure Activity Relationships, QSAR, can be used to relate the chemical characteristics of a molecule to its structure. QSAR methods have been widely used in the pharmaceutical field for drug design, but they are also very useful for many other... 

Martin, Y.C. and Muchmore, S. (2009) Beyond QSAR lead hopping to different structures. QSAR S, Combinatorial Science, 28, 797-801. 

In many cases, at least for screening purposes and for preliminary comparisons of several compounds, approximate information on the intrinsic stability of a molecule, taken as an index of persistence potential that is independent of environmental variables, can be useful. In these cases the use of predictive approaches based on the molecular properties and structure (QSAR quantitative structure-activity relationships) could be very helpful in the absence of experimental information. Although the application of QSARs for the prediction of persistence has not yet been developed for screening as it has for other ecotoxicological aspects (e.g. prediction of toxic effects or bioaccumulation), in the last few years there has been some promising progress (Tremolada et al, 1991 Vasseur etal., 1993 Macalady and Schwarzenbach, 1993). 

By describing the biological or physico-chemical activity of compounds from their chemical structures, QSARs represent a generalization of observations obtained with few compounds, which are assumed to hold for an entire class of chemicals. The change in activity among this series of chemicals is described as a function of the differences in their structures ... 

Martin YC, Muchmore S. Beyond QSAR Lead hopping to different structures. QSAR Comb Sci 2009 28 797-801. 

This is the domain of establishing Structure-Property or Structure-Activity Relationships (SPR or SAR), or even of finding such relationships in a quantitative manner (QSPR or QSAR). 

The search for structural fragments (substructures) is very important in medicinal chemistry, QSAR, spectroscopy, and many other fields in the process of perception of pharmacophore, chromophore, or other -phores. 

All the techniques described above can be used to calculate molecular structures and energies. Which other properties are important for chemoinformatics Most applications have used semi-empirical theory to calculate properties or descriptors, but ab-initio and DFT are equally applicable. In the following, we describe some typical properties and descriptors that have been used in quantitative structure-activity (QSAR) and structure-property (QSPR) relationships. 

To understand the recommendations for structure descriptors in order to be able to apply them in QSAR or drug design in conjunction with statistical methods or machine learning techniques. 

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. 

The method of building predictive models in QSPR/QSAR can also be applied to the modeling of materials without a unique, clearly defined structure. Instead of the connection table, physicochemical data as well as spectra reflecting the compound s structure can be used as molecular descriptors for model building,... 

Besides the aforementioned descriptors, grid-based methods are frequently used in the field of QSAR quantitative structure-activity relationships) [50]. A molecule is placed in a box and for an orthogonal grid of points the interaction energy values between this molecule and another small molecule, such as water, are calculated. The grid map thus obtained characterizes the molecular shape, charge distribution, and hydrophobicity. 

Figure 8-15. Extension of the QSAR method by descriptors not based on structure.

The QSPR/QSAR methodology can also be applied to materials and mixtures where no structural information is available. Instead of descriptors derived from the compound s structure, various physicochemical properties, including spectra, can be used. In particular, spectra are valuable in this context as they reflect the structure in a sensitive way. 

Furthermore, QSPR models for the prediction of free-energy based properties that are based on multilinear regression analysis are often referred to as LFER models, especially, in the wide field of quantitative structure-activity relationships (QSAR). 

The fundamental assumption of SAR and QSAR (Structure-Activity Relationships and Quantitative Structure-Activity Relationships) is that the activity of a compound is related to its structural and/or physicochemical properties. In a classic article Corwin Hansch formulated Eq. (15) as a linear frcc-cncrgy related model for the biological activity (e.g.. toxicity) of a group of congeneric chemicals [37, in which the inverse of C, the concentration effect of the toxicant, is related to a hy-drophobidty term, FI, an electronic term, a (the Hammett substituent constant). Stcric terms can be added to this equation (typically Taft s steric parameter, E,). 

B Mohney and L B Kier 1991. The Electrotopological State An Atom Index for QSAR. ntitative Structure-Activity Relationships 10 43-51. 

E Johansson and M Cocchi 1993. PLS - Partial Least-squares Projections to Latent Structures. In binyi H (Editor) 3D QSAR in Drug Design. Leiden, ESCOM, pp. 523-550. 

Another technique is to use pattern recognition routines. Whereas QSAR relates activity to properties such as the dipole moment, pattern recognition examines only the molecular structure. It thus attempts to find correlations between the functional groups and combinations of functional groups and the biological activity. 

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. 

Like QSAR, molecular structures must be available for compounds that... 

C. Hansch, A. Leo, Exploring QSAR American Chemical Society, Washington (1995). L. B. Kier, L. H. Hall, Molecular Connectivity in Structure-Activity Analysis Research Studies Press, Chichester (1986). 

Practical Applications of Quantitative Structure-Activity Relationships (QSAR) in Environmental Chemistry and Toxicology W. Karcher, J. Devillers, Eds., Kluwer, Dordrecht (1990). 

Once a number of lead compounds have been found, computational and laboratory techniques are very successful in rehning the molecular structures to yield greater drug activity and fewer side elfects. This is done both in the laboratory and computationally by examining the molecular structures to determine which aspects are responsible for both the drug activity and the side effects. These are the QSAR techniques described in Chapter 30. Recently, 3D QSAR has become very popular for this type of application. These techniques have been very successful in the rehnement of lead compounds. 

CODESSA (we tested Version 2.6) stands for comprehensive descriptors for structural and statistical analysis. It is a conventional QSAR/QSPR program. 

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