Quantitative structure-activity defined

Netzeva TI, Worth AP, Aldenberg T, Benigni R, Cronin MTD, Gramatica P et al. Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. The report and recommendations of ECVAM workshop 52. ATLA 2005 33 152-73. 

These are three examples of the use of atomic properties to obtain quantitative structure-activity relationships (QSAR) or structure-function relationships. One should bear in mind that all properties have an atomic basis, making a multitude of new relationships possible. The atomic contribution to the polarizability, for example, is definable and shown to be transferable [26-28], offering the possibility of improving the use of an electrostatic potential map from zero- to first-order estimates of energies of interaction. 

Quantitative Structure-Activity Relationship models are used increasingly in chemical data mining and combinatorial library design [5, 6]. For example, three-dimensional (3-D) stereoelectronic pharmacophore based on QSAR modeling was used recently to search the National Cancer Institute Repository of Small Molecules [7] to find new leads for inhibiting HIV type 1 reverse transcriptase at the nonnucleoside binding site [8]. A descriptor pharmacophore concept was introduced by us recently [9] on the basis of variable selection QSAR the descriptor pharmacophore is defined as a subset of... 

An advantage of defining the problem in this manner is that the partition coefficient has become a central property in quantitative structure-activity relationships (QSAR) and a large data base of P values is available in the medicinal chemistry literature (22-24). In particular, if a correlation (Equation 15) between the polymer-water and octanol-water partition coefficients can be established for a series of solutes, it becomes possible to utilize log P (oc-tanol/water) value as a reference point from which to calculate the polymer-water value. 

Nendza (1998) has defined bioaccumulation as uptake by an organism of a chemical from the environment via any possible pathway, and this can be subdivided into biomagnification (uptake via the food chain) and bioconcentration (uptake from the surrounding milieu). As we shall see, it is the latter that has been the subject of by far the greater number of quantitative structure-activity relationship (QSAR) studies of bioaccumulation. The bioconcentration factor (BCF) is defined as ... 

In addition to using PMs, predictions of toxic hazard can also be made by using structure-activity relationships (SARs). A quantitative structure-activity relationship (QSAR) can be defined as any mathematical model for predicting biological activity from the structure or physicochemical properties of a chemical. In this chapter, the premodifer quantitative is used in accordance with the recommendation of Livingstone (1995) to indicate that a quantitative measure of chemical structure is used. In contrast, a SAR is simply a (qualitative) association between a specific molecular (sub)structure and biological activity. 

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. 

If a cytotoxic chemical is capable of traversing the stratum corneum, it may cause toxicity to the skin as a function of its inherent potential to modify cellular function. Complex quantitative structure activity relationship (QSAR) models developed to assess general cytotoxicity may be applicable to define this inherent toxic potential. The clearest approach to assessing chemical-induced damage to skin is to assess what abnormalities occur when the specific anatomical structures discussed above are perturbed after exposure to topical compounds, since this will be the response modeled in a computational toxicology exercise. 

Quantitative structure-activity or structure-performance (property) relationships (QSAR and QSPR respectively) are of increasing interest in a wide diversity of technology areas. With some notable exceptions, most QSA(P)R studies associated with heterogeneous catalysis are restricted to zeolite-based catalysts. The reasoning is simple -the well defined 3-D structures, of molecular-sieve zeolites, are a reasonable representation of the catalyst "surface", hence bulk characterisation provides information on the catalyst. It is for a similar reason that the majority of modelling studies involves zeolites and zeotypes. 

In the last decades methods have been developed to describe quantitative structure-activity relationships and quantitative structure-property relationships, which deal with the modeling of relationships between structural and chemical or biological properties. The similarity of two compounds concerning their biological activity is one of the central tasks in the development of pharmaceutical products. A typical application is the retrieval of structures with defined biological activity from a database. Biological activity is of special interest in the development of drugs. 

Klein, C.T., Kaiblinger, N. and Wolschann, P. (2002) Internally defined distances in ID-quantitative structure-activity relationships. J. Comput. Aid. Mol. Des., 16, 79-93. 

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