Quantitative structure-selectivity relationship

The abbreviation QSAR stands for quantitative structure-activity relationships. QSPR means quantitative structure-property relationships. As the properties of an organic compound usually cannot be predicted directly from its molecular structure, an indirect approach Is used to overcome this problem. In the first step numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical methods and artificial neural network models are used to predict the property or activity of interest, based on these descriptors or a suitable subset. A typical QSAR/QSPR study comprises the following steps structure entry or start from an existing structure database), descriptor calculation, descriptor selection, model building, model validation. 

Holiday J D, S R Ranade and P Willett 1995. A Fast Algorithm For Selecting Sets Of Dissimilar Molecule From Large Chemical Databases. Quantitative Structure-Activity Relationships 14 501-506. 

Hudson B D, R M Hyde, E Rahr, J Wood and J Osman 1996. Parameter Based Methods for Compoun Selection from Chemical Databases. Quantitative Structure-Activity Relationships 15 285-289. 

JM Sutter, SL Dixon, PC Jurs. Automated descriptor selection for quantitative structure-activity relationships using generalized simulated annealing. I Chem Inf Comput Sci 35(I) 77-84, 1995. 

Zheng W, Tropsha A. Novel variable selection quantitative structure-property relationship approach based on the k-nearest-neighbor principle. J Chem Inf Comput Sci 2000 40(l) 185-94. 

Smith PA, Sorich MJ, McKinnon RA, Miners JO. Pharmacophore and quantitative structure-activity relationship modeling complementary approaches for the rationalization and prediction of UDP-glucuronosyltransferase 1A4 substrate selectivity. J Med Chem 2003 46 1617-26. 

Although the above methodologies proved to be very successful in identifying active kinase inhibitors, they utilized "generic" kinase models and did not address selectivity issues. An interesting recent report has attempted to create quantitative structure-activity relationship (QSAR) models based on data sets of compounds tested against multiple kinases [33]. 

Chen, J., Quan, X., Yan, Y., Yang, F., Peijnenburg, W.J.G.M. (2001) Quantitative structure-property relationship studies on direct photolysis of selected polycyclic aromatic hydrocarbons in atmospheric aerosol. Chemosphere 42, 263-270. 

De Benedetti, P.G., Fanelli, F., Menziani, M.C., Cocchi, M., Testa, R. and Leonardi, A. (1997) Alpha 1-adrenoceptor subtype selectivity molecular modelling and theoretical quantitative structure-affinity relationships. Bioorganic el Medicinal Chemistry, 5, 809-816. 

At present, the selection of an organic modifier is estimated from the aliphatic or aromatic nature of analytes. However, the properties of analytes often cannot be easily obtained. Examples of quantitative structure-retention relationships based on the log-P and van der Waals volume of analytes are demonstrated in Chapter 6. 

The rather time- and cost-expensive preparation of primary brain microvessel endothelial cells, as well as the limited number of experiments which can be performed with intact brain capillaries, has led to an attempt to predict the blood-brain barrier permeability of new chemical entities in silico. Artificial neural networks have been developed to predict the ratios of the steady-state concentrations of drugs in the brain to those of the blood from their structural parameters [117, 118]. A summary of the current efforts is given in Chap. 25. Quantitative structure-property relationship models based on in vivo blood-brain permeation data and systematic variable selection methods led to success rates of prediction of over 80% for barrier permeant and nonper-meant compounds, thus offering a tool for virtual screening of substances of interest [119]. 

Fornal et al. [75] determined selectivity differences for bases in RP-HPLC under high pH conditions. They used quantitative structure retention relationships (QSRR) to model retention behavior. They reported that the stability of the columns they used (Waters XTerra MS, Zorbax Extend, Thermo BetaBasic) was limited with... 

Boehm, M., Stuerzebecher, J., and Klebe, G. Three-dimensional quantitative structure-activity relationship analyses using comparative molecular field analysis and comparative molecular similarity indices analysis to elucidate selectivity differences of inhibitors binding to trypsin, thrombin, and factor Xa. /. Med. Chem. 1999, 42, 458-477. 

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... 

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. 

Key Words Biological activity cell-based partitioning chemical descriptors classification clustering distance-based design diversity selection high-throughput screening quantitative structure-activity relationship. 

Wadkins, R.M., et al. 2004. Discovery of novel selective inhibitors of human intestinal carboxy-lesterase for the amelioration of irinotecan-induced diarrhea Synthesis, quantitative structure-activity relationship analysis, and biological activity. Mol Pharmacol 65 1336. 

The enormous cost of multiple-species, multiple-dose, lifetime evaluations of chronic effects has already made the task of carrying out hazard assessments of all chemicals in commercial use impossible. At the same time, quantitative structure activity relationship (QSAR) studies are not yet predictive enough to indicate which chemicals should be so tested and which chemicals need not be tested. In exposure assessment, continued development of analytical methods will permit ever more sensitive and selective determinations of toxicants in food and the environment, as well as the effects of chemical mixtures and the potential for interactions that affect the ultimate expression of toxicity. Developments in QSARs, in short-term tests based on the expected mechanism of toxic action and simplification of chronic testing procedures, will all be necessary if the chemicals to which the public and the environment are exposed are to be assessed adequately for their potential to cause harm. 

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