Molecular structure QSAR

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. 

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. 

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. 

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

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 reads molecular structure files or output files created by other software packages as the starting point for QSAR analysis. It can import computational results from AMPAC, MOPAC, and Gaussian as well as structures in a number of common formats. 

Most of the 2D QSAR methods are based on graph theoretic indices, which have been extensively studied by Randic [29] and Kier and Hall [30,31]. Although these structural indices represent different aspects of molecular structures, their physicochemical meaning is unclear. Successful applications of these topological indices combined with multiple linear regression (MLR) analysis are summarized in Ref. 31. On the other hand, parameters derived from various experiments through chemometric methods have also been used in the study of peptide QSAR, where partial least square (PLS) [32] analysis has been employed [33]. 

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]. 

Apart from finding structures that give energy minima, most molecular mechanics packages will calculate structural features such as the surface area or the molecular volume. Quantities such as these are often used to investigate relationships between molecular structure and pharmacological activity. This field of human endeavour is called QSAR (quantitative structure and activity relations). 

Many different approaches to QSAR have been developed since Hansch s seminal work. These include both 2D and 3D QSAR methods. The differences among these methods can be reviewed in terms of the two fundamental components of the QSAR approach (1) the structural parameters that are used to characterize molecular structures and (2) the mathematical procedure that is employed to obtain the quantitative relationship between the biological activity and the structural parameters. 

The increased interest in 3D aspects of organic chemistry and quantitative structure-activity relationship (QSAR) studies has caused an increasing need for a much broader access to 3D molecular structures from experiment or calculation. 

United States Environmental Protection Agency (2011) User s Guide for T.E.S.T. version 4.0 A Program to Estimate Toxicity from Molecular Structure United States Environmental Protection Agency, http //www.epa.gov/nrmrl/std/cppb/qsar/. Accessed 09 Mar 2012... 

The US EPA T.E.S.T. is a downloadable program to estimate different toxicological endpoints and physicochemical properties from molecular structure using a variety of QSAR methodologies [58],... 

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). 

De Benedetti, P.G., Menziani, M.C., Cocchi, M. and Fanelli, F. (1995) Prototropic molecular forms and theoretical descriptors in QSAR analysis. Journal of Molecular Structure (Theochemj, 333, 1—17. 

As the method reported is not based on QSAR and thus does not use training information, it cannot be used to predict quantitative inhibition levels (IC50, percentage inhibition or similar information). The method can only be used to find potential mechanism-based inhibitors (binary scale). Despite this limitation, an important advantage over other techniques is that the method suggests the site of the molecule responsible for the inhibition mechanism. With this information researchers may change the molecular structure to maintain activity at the expense of inhibitory effects. 

It is to be noted that the QSPR/QSAR analysis of nanosubstances based on elucidation of molecular structure by the molecular graph is ambiguous due to a large number of atoms involved in these molecular systems. Under such circumstances the chiral vector can be used as elucidation of structure of the carbon nanotubes (Toropov et al., 2007c). The SMILES-like representation information for nanomaterials is also able to provide reasonable good predictive models (Toropov and Leszczynski, 2006a). 

Toropov AA, Benfenati E (2006b) QSAR models for Daphnia toxicity of pesticides based on combinations of topological parameters of molecular structures Bioorg. Med. Chem. 14 2779-2788. 

Currently, karma s rules are formulated in an if-then format. A rule may have multiple conditions, conclusions, and actions, karma takes advantage of both the forward and backward chainers for derivation of the three-dimensional receptor model. For example, two types of rules, generic and specific, can be defined en irically from the results of QSAR as well as from molecular structure. 

Specific rules are based on the attributes of congeners, including the physiochemical parameters used to determine the QSAR equation, the biological activity, and the molecular structure. Backward chaining, using these rules with specific instances of substituents, yields detailed shape and character for the receptor model. For instance, an abstracted specific rule may take the form ... 

---