Quantitative structure-based

Benigni R, Richard AM. Quantitative structure-based modeling applied to characterization and prediction of chemical toxicity. Methods 1998 14 264-76. 

Starting material and the product (Scheme 1.58, middle). Furthermore, the catalyst control of site selectivity was achieved by changing the catalyst to the CFa-modified complex C22. Artemisinin 151 was transformed to ClO-oxi-dized hydroxyl artemisinin 152 under the catalysis of C21. However, when C22 was used, C9-oxidized 9-oxo-artemisinin 153 was obtained as the main product. Furthermore, the development of quantitative structure-based catalyst reactivity models could predict the ratio of the site selectivity (Scheme 1.58, bottom). This discovery should inspire and guide future catalyst design. 

Figure 138. Incorrect qualitative structure of benzo[e]-pyrene, correct qualitative structure of benzofe]pyrene compared to correct quantitative structures of benzo[e]-pyrene based on a77 Kekule valence structures, and correct quantitative structures based on valence structure of the maximal degree of freedom.

A challenging task in material science as well as in pharmaceutical research is to custom tailor a compound s properties. George S. Hammond stated that the most fundamental and lasting objective of synthesis is not production of new compounds, but production of properties (Norris Award Lecture, 1968). The molecular structure of an organic or inorganic compound determines its properties. Nevertheless, methods for the direct prediction of a compound s properties based on its molecular structure are usually not available (Figure 8-1). Therefore, the establishment of Quantitative Structure-Property Relationships (QSPRs) and Quantitative Structure-Activity Relationships (QSARs) uses an indirect approach in order to tackle this problem. In the first step, numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical and artificial neural network models are used to predict the property or activity of interest based on these descriptors or a suitable subset. 

Besides the aforementioned descriptors, grid-based methods are frequently used in the field of QSAR quantitative structure-activity relationships) [50]. A molecule is placed in a box and for an orthogonal grid of points the interaction energy values between this molecule and another small molecule, such as water, are calculated. The grid map thus obtained characterizes the molecular shape, charge distribution, and hydrophobicity. 

Furthermore, QSPR models for the prediction of free-energy based properties that are based on multilinear regression analysis are often referred to as LFER models, especially, in the wide field of quantitative structure-activity relationships (QSAR). 

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. 

PW91 (Perdew, Wang 1991) a gradient corrected DFT method QCI (quadratic conhguration interaction) a correlated ah initio method QMC (quantum Monte Carlo) an explicitly correlated ah initio method QM/MM a technique in which orbital-based calculations and molecular mechanics calculations are combined into one calculation QSAR (quantitative structure-activity relationship) a technique for computing chemical properties, particularly as applied to biological activity QSPR (quantitative structure-property relationship) a technique for computing chemical properties... 

Quantitative Structure-Property Relationships. A useful way to predict physical property data has become available, based only on a knowledge of molecular stmcture, that seems to work well for pyridine compounds. Such a prediction can be used to estimate real physical properties of pyridines without having to synthesize and purify the substance, and then measure the physical property. 

Several methods of quantitative description of molecular structure based on the concepts of valence bond theory have been developed. These methods employ orbitals similar to localized valence bond orbitals, but permitting modest delocalization. These orbitals allow many fewer structures to be considered and remove the need for incorporating many ionic structures, in agreement with chemical intuition. To date, these methods have not been as widely applied in organic chemistry as MO calculations. They have, however, been successfully applied to fundamental structural issues. For example, successful quantitative treatments of the structure and energy of benzene and its heterocyclic analogs have been developed. It remains to be seen whether computations based on DFT and modem valence bond theory will come to rival the widely used MO programs in analysis and interpretation of stmcture and reactivity. 

In this equation, a, b, c, d, and e are regression coefficients. The quantitative structure-reactivity analyses of cyclodextrin inclusion processes are essentially based on this or a similar equation. 

Hammett equation(s) 78, 93, 148ff., 151 f., 153ff., 167f., 190, 193, 196, 297, 299, 308, 312, 375, 381, 392, see also Dual substituent parameter, and Quantitative structure-reactivity relationships Hammond postulate, in additions of nucleophiles to diazonium ions 157 Hard and soft acids/bases principle (Pearson) 49, 54, 109... 

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. 

In general, the described techniques provide an effective, flexible, and relatively fast solution for library design based on analysis of bioscreening data. The quantitative relationships, based on the assessment of contribution values of various molecular descriptors, not only permit the estimation of potential biological activity of candidate compounds before synthesis but also provide information concerning the modification of the structural features necessary for this activity. Usually these techniques are applied in the form of computational filters for constraining the size of virtual combinatorial libraries and... 

Fan Y, Shi LM, Kohn KW, Pommier Y, Weinstein JN. Quantitative structure-antitumor activity relationships of camptothecin analogues cluster analysis and genetic algorithm-based studies. J Med Chem 2001 44 3254-63. 

Blakey GE, Nestorov lA, Arundel PA, Aarons LJ, Rowland M. Quantitative structure-pharmacokinetics relationships I. Development of a whole-body physiologically based model to characterize changes in pharmacokinetics across a homologous series of barbiturates in the rat. J Pharmacokinet Biopharm 1997 Jun 25(3) 277-312. Erratum in J Pharmacokinet Biopharm 1998 Feb 26(l) 131. 

More recently (2006) we performed and reported quantitative structure-activity relationship (QSAR) modeling of the same compounds based on their atomic linear indices, for finding fimctions that discriminate between the tyrosinase inhibitor compounds and inactive ones [50]. Discriminant models have been applied and globally good classifications of 93.51 and 92.46% were observed for nonstochastic and stochastic hnear indices best models, respectively, in the training set. The external prediction sets had accuracies of 91.67 and 89.44% [50]. In addition to this, these fitted models have also been employed in the screening of new cycloartane compounds isolated from herbal plants. Good behavior was observed between the theoretical and experimental results. These results provide a tool that can be used in the identification of new tyrosinase inhibitor compounds [50]. 

Physiologically based pharmacokinetic models provide a format to analyze relationships between model parameters and physicochemical properties for a series of drug analogues. Quantitative structure-pharmacokinetic relationships based on PB-PK model parameters have been pursued [12,13] and may ultimately prove useful in the drug development process. In this venue, such relationships, through predictions of tissue distribution, could expedite drug design and discovery. 

In a study by Andersson et al. [30], the possibilities to use quantitative structure-activity relationship (QSAR) models to predict physical chemical and ecotoxico-logical properties of approximately 200 different plastic additives have been assessed. Physical chemical properties were predicted with the U.S. Environmental Protection Agency Estimation Program Interface (EPI) Suite, Version 3.20. Aquatic ecotoxicity data were calculated by QSAR models in the Toxicity Estimation Software Tool (T.E.S.T.), version 3.3, from U.S. Environmental Protection Agency, as described by Rahmberg et al. [31]. To evaluate the applicability of the QSAR-based characterization factors, they were compared to experiment-based characterization factors for the same substances taken from the USEtox organics database [32], This was done for 39 plastic additives for which experiment-based characterization factors were already available. 

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]. 

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. 

Methods have been presented, with examples, for obtaining quantitative structure-property relationships for alternating conjugated and cross-conjugated dienes and polyenes, and for adjacent dienes and polyenes. The examples include chemical reactivities, chemical properties and physical properties. A method of estimating electrical effect substituent constants for dienyl and polyenyl substituents has been described. The nature of these substituents has been discussed, but unfortunately the discussion is very largely based on estimated values. A full understanding of structural effects on dienyl and polyenyl systems awaits much further experimental study. It would be particularly useful to have more chemical reactivity studies on their substituent effects, and it would be especially helpful if chemical reactivity studies on the transmission of electrical effects in adjacent multiply doubly bonded systems were available. Only further experimental work will show how valid our estimates and predictions are. 

For both nonspecific and structure-based approaches, physicochemical solvation parameters may be used directly, or they may be embedded in quantitative structure-activity relationships.3 This chapter starts with a review of the thermodynamic equations that may be used for a quantitative description of the free energy of solutes in fluid media. Then it provides an... 

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