Quantitative structure - function relationships

Many hydrophobic molecules such as vitamin A, vitamin D and steroid hormones play vital roles in a variety of cellular processes. Because of the low solubility of these molecules in water, it has been difficult to measure the binding properties of the site-directed mutants of the proteins that interact with these hydrophobic ligands such as cellular retinoic acid binding proteins (CRABPs) (Zhang et al. 1992 Chen et al. 1995). This has greatly hampered the studies of the quantitative structure-function relationships of these important proteins. 

As a prerequisite to the determination of the quantitative structure-function relationship it is necessary to define the parameters with which the system of hemoglobin and its ligands is described at the thermodynamic level. The ligand binding curve expresses the thermodynamic properties of the system at equilibrium and therefore this discussion is limited to the thermodynamic equilibrium. 

Rogers D and A J Hopfinger 1994. Application of Genetic Function Approximation to Quantitatir Structure-Activity Relationships and Quantitative Structure-Property Relationships. Journal Chemical Information and Computer Science 34 854-866. 

Structure— Function Relationships. Since PCBs and related HAHs are found in the environment as complex mixtures of isomers and congeners, any meaninghil risk and hazard assessment of these mixtures must consider the quaUtative and quantitative stmcture—function relationships. Several studies have investigated the stmcture—activity relationships for PCBs that exhibit 2,3,7,8-tetrachlorodibenzo-p-dioxin [1746-01-6] (1)... 

D Rogers, AJ Hopflnger. Application of genetic function approximation to quantitative strac-ture-activity relationships and quantitative structure-property relationships. J Chem Inf Comput Sci 34(4) 854-866, 1994. 

ADMET absorption, distribution, metabolism, excretion and toxicity BLW-ED block-localized wave function energy decomposition hERG human ether-a-go-go-related gene QSAR quantitative structure-activity relationship... 

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. 

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. 

To Study interactions between proteins and drugs, an available tool is the Drug Absorption, Distribution, Metabolism, and Excretion (ADME) Associated Protein Database (see Table 1.5). The database contains information about relevant proteins, functions, similarities, substrates and hgands, tissue distributions, and other features of targets. Eor the understanding of pharmacokinetic (PK) and pharmacodynamic (PD) features, some available resources are listed in Table 1.5. For example, the Pharmacokinetic and Pharmacodynamic Resources site provides links to relevant software, courses, textbooks, and journals (see Note 5). For quantitative structure-activity relationship (QSAR), the QSAR Datasets site collects data sets that are available in a structural format (see Table 1.5). 

Rogers, D. Hopfingee, A.J. Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships. J. Chem. Inf. Comput. Sci. 1994, 34, 854-866. Kubinyi, H. Variable selection in QSAR studies. 1. An evolutionary algorithm. Quantum Struct.-Act. Relat. 1994, 13, 285-294. 

Shen, M., Letiran, A., Xiao, Y., Golbraikh, A., Kohn, H., Tropsha, A. Quantitative structure-activity relationship analysis of functionalized amino acid anticonvulsant agents using k nearest neighbor and simulated annealing PLS methods./. Med. Chem. 2002, 45, 2811-2823. 

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