Physicochemical properties, molecular descriptor

Process Molecular Descriptors Physicochemical Properties Thermodynamic and Quantum Chemical Descriptors... 

Figure 2.2 shows some of these methods and how they can be used in combination it also shows the outline of the following sections in this chapter. First, the concept of chemical space, including a sketch of molecular descriptors, physicochemical properties, and databases is presented. Then, a brief description of molecular similarity, pharmacophore modeling, docking, and QSAR models with the incorporation of examples of food-related components is described. These methods are mainly used to develop and analyze SAR and the resultant models can be used to perform virtual screening. Comprehensive reviews of each of these methods are described elsewhere (Alvarez and Shoichet, 2005 Leach and Willet, 2003 Varnek and Tropsha, 2008). 

In both cases, a structural representation of a small molecule is the input parameter to a conceptual set of operations that give rise to numerical outputs such as molecular descriptors, physicochemical properties, or biological outcomes (Fig. 13.1-l(a)). However, to be useful in predictive ways, such as when used to support prospective decisions about the investment of synthetic chemistry resources, at least some of these numerical outputs must be computable given only a structure representation. Only this situation allows relationships between experimentally determined values and computed values to be used to predict experimental outcomes for new molecules, based on their structural similarity to molecules that have already been experimentally tested (Fig. 13.1-l(b)). Most broadly, chemical space is a colloquialism that refers to the ranges and distributions of computed or measured outputs based on chemical structure inputs, and serves as a mathematical framework for quantitative comparisons of similarities and differences between small molecules (Fig. 13.1-l(c)). 

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. 

These first components of the autocorrelation coefficient of the seven physicochemical properties were put together with the other 15 descriptors, providing 22 descriptors. Pairwise correlation analysis was then performed a descriptor was eliminated if the correlation coefficient was equal or higher than 0.90, and four descriptors (molecular weight, the number of carbon atoms, and the first component of the 2D autocorrelation coefficient for the atomic polarizability and n-charge) were removed. This left 18 descriptors. 

A property space can be defined using two classes of descriptors. The first class includes descriptors quantifying the variability (spread) of values their range is probably the most intuitive one. The second class of descriptors relates the dynamic behavior of a given property with other geometric or physicochemical properties. Such correlations can reveal if and how two molecular properties change in a coherent manner. 

The relationship between the herbicidal activity of 1,2,5-oxadiazole iV-oxides and some physicochemical properties potentially related to this bioactivity, such as polarity, molecular volume, proton acceptor ability, lipophilicity, and reduction potential, were studied. The semi-empirical MO method AMI was used to calculate theoretical descriptors such as dipolar moment, molecular volume, Mulliken s charge, and the octanol/water partition coefficients (log Po/w) <2005MOL1197>. 

Wame, M., St. J., Connell, D. W., Hawker, D. W. (1990) Prediction of aqueous solubility and the octanol-water partition coefficient for lipophilic organic compounds using molecular descriptors and physicochemical properties. Chemosphere 16, 109-116. 

Pollutants with high VP tend to concentrate more in the vapor phase as compared to soil or water. Therefore, VP is a key physicochemical property essential for the assessment of chemical distribution in the environment. This property is also used in the design of various chemical engineering processes [49]. Additionally, VP can be used for the estimation of other important physicochemical properties. For example, one can calculate Henry s law constant, soil sorption coefficient, and partition coefficient from VP and aqueous solubility. We were therefore interested to model this important physicochemical property using quantitative structure-property relationships (QSPRs) based on calculated molecular descriptors [27]. 

Molecular descriptors vary gready in both their origins and their applications. They come from both experimental measurements and theoretical computations. Typical molecular descriptors from experimental measurements include logP, aqueous solubility, molar refractivity, dipole moment, polarizability, Hammett substituent constants, and other empirical physicochemical properties. Notice that the majority of experimental descriptors are for entire molecules and come directly from experimental measurements. A few of them, such as various substituent constants, are for molecular fragments attached to certain molecular templates and they are derived from experimental results. 

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