Quantitative structure-activity relationship calculation

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. 

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... 

The increased interest in 3D aspects of organic chemistry and quantitative structure-activity relationship (QSAR) studies has caused an increasing need for a much broader access to 3D molecular structures from experiment or calculation. 

The CHI index is reportedly a relevant parameter in quantitative structure-activity relationship (QSAR) studies [41]. With this approach, log P could be determined in the range -0.45more than 25000 compounds with excellent reproducibility (within 2 index units) and reported in a GlaxoSmithKline database [11]. Two main drawbacks were identified using this approach (i) the assumptions used in Ref [7], i.e. that S is constant for all compounds and that the system dwell volume is excluded in calculations, yield some discrepancies in the resulting log P, and (ii) the set of gradient calibration... 

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. 

Aleksic et al. [47] estimated the hydrophobicity of miconazole and other antimycotic drugs by a planar chromatographic method. The retention behavior of the drugs have been determined by TLC by using the binary mobile phases acetone-n-hexane, methanol toluene, and methyl ethyl ketone toluene containing different amounts of organic modifier. Hydrophobicity was established from the linear relationships between the solute RM values and the concentration of organic modifier. Calculated values of RMO and CO were considered for application in quantitative structure activity relationship studies of the antimycotics. 

C-H and N-H bond dissociation energies (BDEs) of various five- and six-membered ring aromatic compounds (including 1,2,5-oxadiazole) were calculated using composite ab initio CBS-Q, G3, and G3B3 methods. It was found that all these composite ab initio methods provided very similar BDEs, despite the fact that different geometries and different procedures in the extrapolation to complete incorporation of electron correlation and complete basis set limit were used. A good quantitive structure-activity relationship (QSAR) model for the C-H BDEs of aromatic compounds... 

Thiadiazole 1 and its derivatives were used as model compounds for the calculation of molecular parameters related to physical properties for their use in quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) studies <1999EJM41, 2003IJB2583, 2005JMT27>. 

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). 

QSAR. Quantitative Structure Activity Relationships. The name given to attempts to relate measured or calculated properties to molecular structure. 

Computational chemistry methodology is finding increasing application to the design of new flavoring agents. This chapter surveys several useful techniques linear free energy relationships, quantitative structure-activity relationships, conformational analysis, electronic structure calculations, and statistical methods. Applications to the study of artificial sweeteners are described. 

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. 

Gruber, C. and Buh, V. (1989) Quantum mechanically calculated properties for the development of quantitative structure-activity relationships (QSARs). pfCa values of phenols and aromatic and aliphatic carboxylic acids. Chemosphere, 19, 1595-1609. 

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