Quantitative structure-activity relationship structural descriptors

Methods other than thermodynamic cycles are often used to calculate acid dissociation constants. Previous publications implement the theoretical relationship between pKa and structural property [6], bond valence methods and bond lengths [33], pKa correlations with highest occupied molecular orbital (HOMO) energies and frontier molecular orbitals [34], and artificial neural networks [35] to predict pKa values. In addition much work has been done using physical properties as quantitative structure-activity relationship (QSAR) descriptors, and regression equations with such descriptors to yield accurate pKa values for specific classes of molecules [36-47]. The correlation of pKas to various molecular properties, however, is often restricted to specific classes of compounds, and it is... 

Kim, K.H. (1993d) Separation of electronic, hydrophobic, and steric effects in 3D quantitative structure-activity relationships with descriptors direcdy from 3D structures using a comparative molecular field analysis (CoMFA) approach. Curr. Top. Med. Chem., 1, 453-467. 

All the techniques described above can be used to calculate molecular structures and energies. Which other properties are important for chemoinformatics Most applications have used semi-empirical theory to calculate properties or descriptors, but ab-initio and DFT are equally applicable. In the following, we describe some typical properties and descriptors that have been used in quantitative structure-activity (QSAR) and structure-property (QSPR) relationships. 

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. 

Dunn W J III, S Wold, U Edlund, S Hellberg and J Gasteiger 1984. Multivariate Structure-Activib Relationships Between Data from a Battery of Biological Tests and an Ensemble of Structur Descriptors The PLS Method. Quantitative Structure-Activity Relationships 3 131-137. 

Some properties, such as the molecular size, can be computed directly from the molecular geometry. This is particularly important, because these properties are accessible from molecular mechanics calculations. Many descriptors for quantitative structure activity or property relationship calculations can be computed from the geometry only. 

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. 

With the development of accurate computational methods for generating 3D conformations of chemical structures, QSAR approaches that employ 3D descriptors have been developed to address the problems of 2D QSAR techniques, that is, their inability to distinguish stereoisomers. Examples of 3D QSAR include molecular shape analysis (MSA) [26], distance geometry,and Voronoi techniques [27]. The MSA method utilizes shape descriptors and MLR analysis, whereas the other two approaches apply atomic refractivity as structural descriptor and the solution of mathematical inequalities to obtain the quantitative relationships. These methods have been applied to study structure-activity relationships of many data sets by Hopfinger and Crippen, respectively. Perhaps the most popular example of the 3D QSAR is the com-... 

Bagchi, M. C., Mills, D., Basak, S. C. Quantitative structure-activity relationship (QSAR) studies of quinolone antibacterials against M. fortuitum and M. smegmatis using theoretical molecular descriptors. 

I 6 H-bonding Parameterization in Quantitative Structure-Activity Relationships di Drug Design Tab. 6.1 Two-dimensional H-bond descriptors. 

Wilson, L. Y., Famini, G. R. Using theoretical descriptors in quantitative structure-activity relationships some... 

Raevsky, O. A., Schaper, K.-J. Physicochemical descriptors governing the solubility and partitioning of chemicals in water-solvent-gas systems. In Abstracts of 12th International Workshop on Quantitative Structure-Activity Relationships in Environmental Toxicology, Lyon, France, 2006, p. 23. 

C. N., Boutina, D., Beck, G., Sherbom, B., Cooper, J., Platts, J. A. Evaluation of human intestinal absorption data and subsequent derivation of a quantitative structure-activity relationship (QSAR) with the Abraham descriptors. J. Pharm. Sci. 2001, 90, 749-784. 

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. 

Dearden, J. C. (1990) Physico-chemical descriptors. In Practical Applications of Quantitative Structure-Activity Relationships (QSAR) in Environmental Chemistry and Toxicology. Karcher, W. and Devillers, J., Eds., pp. 25-60. Kluwer Academic Publisher, Dordrecht, The Netherlands. 

M. H. Abraham, New solute descriptors for linear free energy relationships and quantitative structure-activity relationships, in Quantitative Treatments of Solute/Solvent Interactions, P. Politzer and J. S. Murray, eds., Elsevier, Amsterdam (1994) pp. 83-134. 

The octanol-water partition coefficient, Kow, is the most widely used descriptor of hydrophobicity in quantitative structure activity relationships (QSAR), which are used to describe sorption to organic matter, soil, and sediments [15], bioaccumulation [104], and toxicity [105 107J. Octanol is an amphiphilic bulk solvent with a molar volume of 0.12 dm3 mol when saturated with water. In the octanol-water system, octanol contains 2.3 mol dm 3 of water (one molecule of water per four molecules of octanol) and water is saturated with 4.5 x 10-3 mol dm 3 octanol. Octanol is more suitable than any other solvent system (for) mimicking biological membranes and organic matter properties, because it contains an aliphatic alkyl chain for pure van der Waals interactions plus the alcohol group, which can act as a hydrogen donor and acceptor. 

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