QSAR quantitative structure-activity deriving equation

QSAR. Quantitative structure-activity relationships—the science of deriving quantitative linear or nonlinear mathematical relationships between physicochemical and topo-logical/topographical properties of chemical structures and their biological activity. Originally, regression analysis was the only tool used to derive QSAR equations. More re-... 

The correlation of bioactivity data with Eq. 1 or some relationship derived from it results, if successful, in a correlation equation called a quantitative structure activity relationship (QSAR). 

Quantitative Structure Activity Relationship (QSAR) is a method that makes predictions by the quantitative description of molecular properties with the use of descriptors of the chemical structure (Dearden 2003). This means QSAR models describe the quantitative or calculated relationship between a chemical structure and their biological activity (e.g. toxicity) with the help of chemical descriptors that are generated from the molecular structure (Durham and Pearl 2001). This relationship is described in from of a mathematical equation (e.g. log 1/C = a tt + b a +. .. + const). QSAR models generally show better predictivity if all compounds of a dataset involved in the prediction are derived from a congeneric series of compounds, that means they should all act by the same mechanism of action, since the physico-chemical and structural descriptors used in the QSAR reflect the same mechanism of action. Sometimes it is difficult to determine the mechanism of action, so series of compounds involved in a QSAR model are often restricted to a given chemical class in the hope that this will ensure a single mechanism of action (Dearden 2003). 

A computational method of the structure prediction of an inhibitor is based on an analysis of the quantitative structure-activity relationship (QSAR) (Ariens, 1989 Martin et al, 1996). In this method, quantities such as volume, hydrophobicity or a number of specific groups are experimentally derived. QSAR for a given TS is a polynomial equation with n terms. Each of these terms corresponds to the number of aforementioned regions of a particular molecule under investigation. In the framework of this approach, it is necessary to define, prior to synthesis and testing, a functional relationship between molecular structure and molecular action. Then the polynomial equation can be used to predict the inhibition constant of molecules that have been not tested experimentally. 

Hansch and Caldwell have analyzed the quantitative structure/activity relationships (QSAR) of a series of amphetamine and 2-phenethylamine analogs, to discern the role of steric and hydrophobic aryl substituents on the inhibition of 5-HT uptake (142). From the biological data of 19 compounds, including those in Table 15.13. and some additional analogs, the following equation was derived for inhibition of uptake activity, where C is the IC concentration, MR4 is the molar refrac-tivity value of the aryl substituent scaled by 0.1, and 7T3 is the hydrophobicity of the meUi substituent on the aryl ring ... 

In terms of practical application, expert systems overlap with systems for deriving and applying quantitative structure-activity relationship (QSAR) models or equations, and with systems using artificial neural networks (ANN) or genetic algorithms. The expert systems described in this chapter are characterized by their use of a generalized store of knowledge. 

Quantitative structure-activity relationship (QSAR) studies express the biological activities of compounds as functions of their various chemical descriptors. Essentially, they describe how biological activity variation depends on changes of chemical structure [1, 2], If a clearly defined relationship can be derived from the structure-activity data, the model equation allows chemists to determine with some confidence which physicochemical properties play an important role in biological activity, and thereby to attempt predictions. By quantifying physicochemical properties, it should then be possible to calculate in advance the biological activity of a novel compound. 

Also in chemistry artificial neural networks have found wide use. They have been used to fit spectroscopic data, to investigate quantitative structure-activity relationships (QSAR), to predict deposition rates in chemical vapor deposition, to predict binding sites of biomolecules, to derive pair potentials from diffraction data on liquids, " to solve the Schrodinger equation for simple model potentials like the harmonic oscillator, to estimate the fitness function in genetic algorithm optimizations, in experimental data analysis, to predict the secondary structure of proteins, to predict atomic energy levels, " and to solve classification problems from clinical chemistry, in particular the differentiation between diseases on the basis of characteristic laboratory data. ... 

In many cases of practical interest, no theoretically based mathematical equations exist for the relationships between x and y we sometimes know but often only assume that relationships exist. Examples are for instance modeling of the boiling point or the toxicity of chemical compounds by variables derived from the chemical structure (molecular descriptors). Investigation of quantitative structure-property or structure-activity relationships (QSPR/QSAR) by this approach requires multivariate calibration methods. For such purely empirical models—often with many variables—the... 

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