Modeling Quantitative Structure-Reactivity

In summary, the overall rate of reductive dehalogenation of a given compound in a given system may be determined by various rather complex steps, and may, therefore, be influenced by several compound properties. Furthermore, even within a series of structurally related compounds, the relative importance of the various steps may differ, thus rendering any quantitative structure reactivity relationships (QSARs) rather difficult. This also means that calibration of a given system with a small set of model compounds for estimating absolute reaction rates will be even more difficult as compared to the situation with NAC reduction (see above). 

In short, each reaction family could be described with a maximiun of three parameters (A, Eo, a). Procurement of a rate constant from these parameters required only an estimate of the enthalpy change of reaction for each elementary step. In principle, this enthalpy change of reaction amoimted to the simple calculation of the difference between the heats of formation of the products and reactants. However, since many model species, particularly the ionic intermediates and olefins, were without experimental values, a computational chemistry package, MOPAC, ° was used to estimate the heat of formations on the fly . Ihe organization of the rate constants into quantitative structure-reactivity correlations (QSRC) reduced the number of model parameters greatly Ifom O(IO ) to 0(10). 

An alternative viewpoint for structure-activity investigations is to utilize quantitative models as probes into the mechanism of action of the set of compounds being studied. In this case it is most useful if the molecular descriptors are explicitly meaningful in terms of chemical reactivity or physiological behavior, e.g., distribution of the compound in an organism (see Table II). In a previous symposium, (18), we described our application of this approach toward the development of a quantitative structure-potency expression, equation 1,... 

As the chemical models mentioned here refer to some fundamental thermochemical and electronic effects of molecules, their application is not restricted to the prediction of chemical reactivity data. In fact, in the development of the models extensive comparisons were made with physical data, and thus such data can also be predicted from our models. Furthermore, some of the mechanisms responsible for binding substrates to receptors are naturally enough founded on quite similar electronic effects to those responsible for chemical reactivity. This suggest the use of the models developed here to calculate parameters for quantitative structure-activity relationships (QSAR). 

In a parallel development, structural effects on the chemical reactivity and physical properties of organic compounds were modelled quantitatively by the Hammett equation 8). The topic is well reviewed by Shorter 9>. Hansen 10) attempted to apply the Hammett equation to biological activities, while Zahradnik U) suggested an analogous equation applicable to biological activities. The major step forward is due to the work of Hansch and Fujita12), who showed that a correlation equation which accounted for both electrical and hydrophobic effects could successfully model bioactivities. In later work, steric parameters were included 13). 

There are several properties of a chemical that are related to exposure potential or overall reactivity for which structure-based predictive models are available. The relevant properties discussed here are bioaccumulation, oral, dermal, and inhalation bioavailability and reactivity. These prediction methods are based on a combination of in vitro assays and quantitative structure-activity relationships (QSARs) [3]. QSARs are simple, usually linear, mathematical models that use chemical structure descriptors to predict first-order physicochemical properties, such as water solubility. Other, similar models can then be constructed that use the first-order physicochemical properties to predict more complex properties, including those of interest here. Chemical descriptors are properties that can be calculated directly from a chemical structure graph and can include abstract quantities, such as connectivity indices, or more intuitive properties, such as dipole moment or total surface area. QSAR models are parameterized using training data from sets of chemicals for which both structure and chemical properties are known, and are validated against other (independent) sets of chemicals. 

However, Edwin A. Abbott s fanciful two-dimensional world described in Flat-land is no more the world of molecules and chemical reactions than it was the world around us. While molecular topology is adequate to explain many aspects of chemical behavior, the evolution of quantitative models for structure-activity relations is unavoidably moving into the realm of three-dimensional (3D) structure. Modern computing enables rapid manipulation of 3D chemical structures, and it is leading the way for a proliferation of models for the quantification of electronic structure. For complex behavior of chemicals including physicochemical properties, reactivity, and biological activity, 3D structures are essential. 

Fig. 3 Schematic of the different aspects of surface functionalization, patterning and analysis treated in this review. The topic is introduced and developed starting from the discussion of well-defined model systems (SAMs on Au). The determination of structure-reactivity relationships, and in particular the way conformational order affects the reactivity of NHS active esters will be discussed. Using iCFM, very localized information on surface reactions can be quantitatively measured in situ for SAM-based systems. The extension of the dimensionality to quasi-3D systems via the immobilization of den-drimers and the fabrication of thin reactive homopolymer films will be addressed, as well as micro- and nanopatterning approaches via soft and scanning probe lithography. Here we discuss SAM-based, as well as bilayer/vesicle-based systems...

Abstract Quantitative structure-activity relationship (QSAR) models are generated for biological activity and toxicity in terms of global and local reactivity descriptors within a conceptual density functional theory framework. Possible anticancer activity of two new metal-borane clusters is analyzed. 

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. 

However equally important is the availability of physically grounded models that can provide understanding of chemical reactivity. In the past, theories of reactivity have been based on empirical structure-reactivity relationships (viz. linear free energy relationships) or qualitative theoretical concepts (viz. Woodward-Hoffmann approach or the frontier orbital method)[4-ll]. However, there is a different approach, which is potentially more fruitful. In this approach one uses physically grounded models that can be obtained from the best state of the art methodology of quantum chemistry. These physically grounded models must be both quantitative and qualitative. On the one hand, any model used should reproduce the numerically computed quantities exactly, on the... 

During the past 20 years, academic and industrial researchers developed composition-based kinetic models with hundreds or even thousands of lumps and pure compounds. The QSRC (quantitative stracture-reactivity correlation) and LFER (linear free energy relationship) lumping techniques are discussed in Chapter 20 by Professor Klein and Chapter 9 by Professor Mochida. The structure-oriented liunping (SOL) approach of Quann and Jaffe yields models rigorous enough for use in closed-loop real-time optimizers (CLRTO), which automatically adjust setpoints for commercial process units several times each day. ... 

TABLE 3.25 Statistics of the bivariate quantitative structure-transition energy reactivity models computed for the molecules of Figure 3.14 with data of Table 3.24 (Putz et al., 2008). 

---