Descriptor hydrophobicity

Pharma- cokinetic parameter logK lAM Hydrophobicity descriptor l°g K sFod. log K dg ... 

Hydrophobicity (or lipophilicity) characterizes the readiness of a molecule to escape or to prefer the water environment. It plays a fundamental role in biochemical processes and influences the fate of a molecule in the environment. Thus, hydrophobic descriptors play an important role in QSAR modeling that is used in drug research and for risk characterization. The most widely used hydrophobic descriptor is the octanol-water partition coefficient (log P) proposed by Hansh [49]. P is a quotient between solubihties in octanol and water. It is defined by following equation ... 

The most commonly exploited QSRR are those relating reversed-phase HPLC and TLC retention parameters to log P. Many literature reports have demonstrated the successful application of chromatographically derived hydrophobicity descriptors in medicinal, agricultural and environmental chemistry 7,12,13,80.107,I20. ... 

Description Calculation of steric, electronic, and hydrophobic descriptors. 

Tanaka, A. and Fujiwara, H. (1996). Quantitative Structure-Activity Relationship Study of Fibrinogen Inhibitors, ((4-(4-Amidinophenoxy)Butanoyl)Aspartyl)Valine (FK633) Derivatives, Using a Novel Hydrophobic Descriptor. J.MetLChem., 39,5017-5020. 

Keseru [35] used literature data on 55 compounds to train a QSAR model based on a number of calculated descriptors. Five descriptors were used clogP, calculated molar refractivity (CMR), partial negative surface area, and the VolSurf W2 (polarizability) and D3 (hydrophobicity) descriptors. A model of acceptable quality was obtained (f = 0.94, SSE = 0.82) and tested on a 13 compound holdout set (r2 = 0.56, SSE = 0.98). An HQSAR model was then created that made use of 2D fragment fingerprints (threshold hERG IC50 = lpM). The best HQSAR model was validated on a holdout set of 13 compounds (f = 0.81, SSE = 0.67). 

VolSurf descriptors of hydrophobic regions are hydrophobic descriptors defined in terms of the volume of the molecular envelope generating attractive hydrophobic interactions and best hydrophobic volumes that represent the best three hydrophobic interactions generated by the DRY probe and measured at —0.6 and —1.0kcal/mol. VolSurf computes hydrophobic... 

Tanaka, A. and Fujiwara, FI. (1996) Quantitative structure-activity relationship study of fibrinogen inhibitors ((4-(4-amidinophenoxy)butanoyl) aspartyljvaline (FK633) derivatives, using a novel hydrophobic descriptor. /. Med. Chem.,... 

The relationship between individual components appears to indicate that the first and second SaSA dimensions can be related to the first and third ALMOND dimensions, with an emphasis on the first component. The first SaSA component is dominated by size-related descriptors, e.g., the total number of heavy atoms, MW, CMR, and polarizability. These properties explain approximately 60% of the first ALMOND component, suggesting that large internode distances and high internode interaction energies dominate this component. The third ALMOND component has a weaker relationship (25%) to hydrophobicity descriptors such as CLOGP, ELogP, nonpolar surface area, and the number of nonpolar atoms [123]. Only the fourth ALMOND component appears to be related (under 25%) with hydrogen-bond-related, e.g., HYBOT descriptors, whereas the second, fifth, and sixth ALMOND... 

The electronic, steric, and hydrophobic descriptors described above are rooted in the physical chemistry of intermolecular interactions. It is also possible to calculate molecular descriptors that are not based on physical chemistry, but rather are just a consideration of the 2D structure of the molecule. Because the physical properties may be calculated from the 2D structure of the molecule, it follows that the structure contains this information and that consideration of... 

The impact of hydrophobicity on most biological processes is of great significance as can be ascertained by the frequency of its usage in most QSAR studies. Despite the early usage by Meyer and Overton at the end of the nineteenth century, hydrophobicity eventually took its cues from the Hammett approach and finally crystallized fifty years later when the best known and now well-developed hydrophobic descriptor, log Po/w was delineated by Hansch and Fujita. 

A survey of the QSAR journal in 1988 showed that 40% of the reported models involved a hydrophobicity descriptor, rising to more than 50% in a similar survey ten years later in 1998. 

In addition to the electronic (o), steric (Es) and hydrophobic (it) descriptors (Table 11.12), other physical and chemical descriptors such as electronic descriptors (e.g. ionization constants pKa, NMR chemical shifts 8) steric descriptors (e.g. molar volume MV, van der Waals volume V, molar refractivity MR, parachor P,), hydrophobic descriptors (e.g. distribution coefficient logD, solubilty parameter logS), property descriptors (e.g. T of DNA) and theoretical parameters (e.g. atomic charge q°or q", electrostatic potential V(r)) are also employed in the QSAR studies of chemical and biological systems. 

In the defining equation for the partition coefficient it was noted that the concentration terms referred to the concentration of the same species. This can have significance for the measurement of log P if some interaction (for example, dimerization) occurs predominantly in one phase, but is probably of most significance if the molecule contains an ionizable group. Since P refers to one species it is necessary to suppress ionization by the use of a suitable pH for the aqueous phase. An alternative is to measure a distribution coefficient, D, which involves the concentrations of both ionized and un-ionized species, and apply a correction factor based on the pK values of the group(s) involved. Yet another alternative is to use log D values themselves as a hydrophobic descriptor, although this may suffer from the disadvantage that it includes electronic information. 

Non-linear models may be fitted to data sets by the inclusion of functions of physicochemical parameters in a linear regression model—for example, an equation in n and as shown in Fig. 6.5—or by the use of non-linear fitting methods. The latter topic is outside the scope of this book but is well covered in many statistical texts (e.g. Draper and Smith 1981). Construction of linear regression models containing non-linear terms is most often prompted when the data is clearly not well fitted by a linear model, e.g. Fig. 6.4e, but where regularity in the data suggests that some other model will fit. A very common example in the field of quantitative structure-activity relationship (QSAR) involves non-linear relationships with hydrophobic descriptors such as log P or n. Non-linear dependency of biological properties on these parameters became apparent early in the... 

M. C. Nicklaus, H. Ford Jr., L. Hegedus, G. W. A. Milne, and J. A. Kelley, Quant. Struct. Act. Relat., 14,335 (1995). Comparative Molecular-Field Analysis of Hydrophobicity Descriptors of Cytosine Nucleosides. 

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