Hansch-type QSAR

The first approach mentioned was chosen in the C-QSAR database [34] using Hansch-type QSAR equations [35], Its advantage is that these models are easy to apply and interpret. However, each model has a limited applicability domain (Figure 12.1) thus only toxicity predictions for compounds belonging to these specific classes are suitable. Unfortunately, no analysis of the predictive power of a test set has been published to date. 

This was followed by Hansch-type QSAR studies with propafenone analogs... 

Logarithmic transformation yields the general expression for Hansch-type QSARs ... 

Sometimes, the derivation of a simple Hansch equation is the result of a complex series of analyses and hypotheses on the mechanisms underlying the biological activity/property under study. The final equation thus summarizes such preliminary work. The correct application of Hansch-type QSARs provides a powerful tool for estimating parameters in environmental sciences and its prevailing efficiency is illustrated throughout Part 2. 

Let us consider a pair of regression equations to illustrate the qualitative nature of them. The selected equation, which represents the Hansch-type QSAR, is... 

Although this first major, widespread effort at SBDD was a disappointment, the situation looked better on the QSAR front. In Japan, Koga " " employed classical (Hansch-type) QSAR while discovering the antibacterial agent norfloxacin around 1982. Norfloxacin was the first of the third-generation analogs of nalidixic... 

The statistical methods of QSAR modelling are based on the correlation of changes in biological/toxicological activity (AO) resulting from certain chemical modifications (AC), either directly by structural parameters, called Free Wilson-type relationships, or by the corresponding changes of molecular properties, called Hansch-type analyses (Kubinyi 2002) ... 

The classical QSAR methodology started 1964 with the publications of Hansch and Fujita (1964) and Free and Wilson (1964) and the statement of Hansch (1969) resulted from a proposal by Fujita. They proposed to combine several physiochemical parameters (tt, a), also called descriptors, in a quantitative model. This Hansch-type analysis is very flexible and describes many different kinds of biological activities, e.g. in vitro data such as enzyme inhibition (Kubinyi 2002) ... 

It was also shown that the "ip index could be used as a significant second variable in the QSAR equation. Hansch-type analysis yielded r = 0.90. ... 

Molecular simulations, including energy minimizations, dynamics, and firee energy perturbation calculations on macromolecules. Quest for ab initio molecular orbital calculations. Ultimol for molecular visualizations. Galaxy for Hansch-type and 3D QSAR and molecular modeling. 

Still, whatever the computational procedure approached, either of that of Hansch type, 3D, decisional, or orthogonal ones, see the Section 2.5 of this Conclusion for detailed presentation, the problem of delivering the molecular interaction mechanism as a QSAR analysis result was... 

Ob-6 AvatyCO 1) In the QSAR-context, it is necessary and sufficient that p verifies conditions C2), i.e., to be a dissimilarity index. In this sense MSD was used as a steric parameter (instead of the Taft Eg constant, for example) in Hansch type structure-biological activity correlations. 2) These results obtained for MSD are valid... 

Neural networks have been introduced in QSAR for non-linear Hansch analyses. The Perceptron, which is generally considered as a forerunner of neural networks has been developed by the Russian school of Rastrigin and coworkers [62] within the context of QSAR. The learning machine is another prototype of neural network which has been introduced in QSAR by Jurs et al. [63] for the discrimination between different types of compounds on the basis of their properties. 

These efforts were guided by the study of quantitative structure-activity relationships (QSAR) following the Hansch approach. In this method linear free-energy related and other electronic, hydrophobic, and steric substituent constants are used for a quantitative analysis of the possible ways in which substituents may modulate bioactivity in a congeneric series. In the QSAR studies of benzoylphenyl ureas the electronic Hammett a-constants and the hydro-phobic Hansch n-constants were used. To measure the steric influences, steric substituent constants of a new type (B1,B2,B3,B4, and L) were applied which had recently been introduced by us and which give improved correlations in comparison with the steric Es constants used in the literature hitherto (21, 22). The constants B- toBj are measures of the widths of substituents in four rectangular directions. The L-constant accounts for the length of a substituent ... 

The epoch of QSAR (Quantitative Structure-Activity Relationships) studies began in 1963-1964 with two seminal approaches the a-p-7i analysis of Hansch and Fujita " and the Free-Wilson method. The former approach involves three types of descriptors related to electronic, steric and hydrophobic characteristics of substituents, whereas the latter considers the substituents themselves as descriptors. Both approaches are confined to strictly congeneric series of compounds. The Free Wilson method additionally requires all types of substituents to be suflficiently present in the training set. A combination of these two approaches has led to QSAR models involving indicator variables, which indicate the presence of some structural fragments in molecules. 

Many different approaches to QSAR have been developed since Hansch s seminal work. As briefly discussed above, the major differences between these methods can be analyzed from two viewpoints (1) the types of structural parameters that are used to characterize molecular identities, starting from different representation of molecules, from simple chemical formulas to three-dimensional conformations and (2) the mathematical procedure that is employed to obtain the quantitative relationship between these structural parameters and biological activity. 

On the basis of the origin of molecular descriptors used in calculations, QSAR methods can be divided into three groups. One group is based on a relatively small number (usually many times smaller than the number of compounds in a data set) of physicochemical properties and parameters describing,for example, hydrophobic, steric, and electrostatic effects. Usually, these descriptors are used as independent variables in multiple regression approaches (18) Jn the literature, these methods are typically referred to as Hansch analysis (8).These types of descriptors and corresponding linear optimization methods used in traditional QSAR analyses are discussed extensively in the chapter by Celassie (7) and therefore is not reviewed here. 

Since the early work of Hansch et al., numerous examples of the quantitative correlation of biological activity with chemical structure have been reported. The success of QSAR relies heavily on the use of partition coefficients (log P or n) in extending the linear free-energy relationship (LEER) from homogenous organic chemical systems (i.e., the Hammett-Taft type approach) to compartmentalized heterogenous biological systems. 

Tmej C, Chiba P, Huber M, Richter E, Hitzler M, Schaper KJ, Ecker G. A combined Hansch/free-Wilson approach as predictive tool in QSAR studies on propafenone-type modulators of multidrug resistance. Arch Pharm (Weinheim) 1998 331 233 10. 

---