Virtual physicochemical properties

An extensive series of studies for the prediction of aqueous solubility has been reported in the literature, as summarized by Lipinski et al. [15] and jorgensen and Duffy [16]. These methods can be categorized into three types 1 correlation of solubility with experimentally determined physicochemical properties such as melting point and molecular volume 2) estimation of solubility by group contribution methods and 3) correlation of solubility with descriptors derived from the molecular structure by computational methods. The third approach has been proven to be particularly successful for the prediction of solubility because it does not need experimental descriptors and can therefore be applied to collections of virtual compounds also. 

Prediction of various physicochemical properties such as solubihty, lipophhicity log P, pfQ, number of H-donor and acceptor atoms, number of rotatable bonds, polar surface area), drug-likeness, lead-likeness, and pharmacokinetic properties (ADMET profile). These properties can be applied as a filter in the prescreening step in virtual screening. 

Morris, J. J., Bruneau, P. Prediction of physicochemical properties. In Virtual Screening for Bioactive Molecules, Bohm,... 

The virtual library was then characterized using the Cerius2 default topological descriptors and physicochemical properties (35). The 50 default descriptors were reduced to three principal components using principal components analysis, and this defined a 3D chemistry space into which the virtual library could be plotted. The chemistry space consisted of 1134 cells and, when the virtual library was mapped into the space, it was found to occupy 364 of the cells thus, this represents the maximum cell coverage that is achievable. 

Fink, T. and Reymond, J.-L. (2007) Virtual exploration of the chemical universe up to 11 atoms of C, N, O, F assembly of 26.4 million structures (110.9 million stereoisomers) and analysis for new ring systems, stereochemistry, physicochemical properties, compound classes, and drug discovery. Journal of Chemical Information and Computer Sciences, 47, 342-353. 

The experimental setup of the SSM is shown in Fig. 19.12a. The temporal increase of the aqueous concentration of four diesel fuel components (benzene, m/p-xylene, naphthalene) is given in Figs. 19.126 to d. Relevant physicochemical properties are summarized in Table 19.3. Note that the two isomers m- and /7-xylene exhibit virtually the same properties, and are, therefore, considered together. 

These treatments manipulate selected physicochemical properties of pollutants in such a way that they are either rendered less harmful (or even virtually harmless in some cases) or else removed from the target medium. They are based on redox, acid-base, complexation, electrochemical, solubility, and catalytic principles, as discussed below. 

Simplistic and heuristic similarity-based approaches can hardly produce as good predictive models as modern statistical and machine learning methods that are able to assess quantitatively biological or physicochemical properties. QSAR-based virtual screening consists of direct assessment of activity values (numerical or binary) of all compounds in the database followed by selection of hits possessing desirable activity. Mathematical methods used for models preparation can be subdivided into classification and regression approaches. The former decide whether a given compound is active, whereas the latter numerically evaluate the activity values. Classification approaches that assess probability of decisions are called probabilistic. 

Morris JJ, Bruneau PP. Prediction of physicochemical properties. In Bohm HJ, Schneider G, editors, Virtual screening for bioactive molecules. Weinheim Wiley-VCH, 2000. p. 33-58. 

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