Jurs Descriptors

The 3D descriptors described in this section are the weighted holistic invariant molecular (WHIM) descriptors, the Jurs descriptors, and the GRID-based VolSurf and Almond descriptors, as well as pharmacophore fingerprints. 

The so-called Jurs descriptors are 3D surface descriptions related to various total and fractional defined surfaces. They can be divided into to two parts one electronic [35] and one hydrophobic [36]. The former set of descriptors is generated from partial positive and negative surface areas, total charge as well as atomic positively and negatively charged weighted surface areas, and various differential and fractional charged partial surface areas of the molecule (see Table 14.3). 

Table 14.3 List of selected electronic Jurs descriptors.

The Jurs descriptors have been found useful in modeling ADM ETproperties such as human intestinal absorption [25, 39] and toxicity [40]. 

The MEP at the molecular surface has been used for many QSAR and QSPR applications. Quantum mechanically calculated MEPs are more detailed and accurate at the important areas of the surface than those derived from net atomic charges and are therefore usually preferable [Ij. However, any of the techniques based on MEPs calculated from net atomic charges can be used for full quantum mechanical calculations, and vice versa. The best-known descriptors based on the statistics of the MEP at the molecular surface are those introduced by Murray and Politzer [44]. These were originally formulated for DFT calculations using an isodensity surface. They have also been used very extensively with semi-empirical MO techniques and solvent-accessible surfaces [1, 2]. The charged polar surface area (CPSA) descriptors proposed by Stanton and Jurs [45] are also based on charges derived from semi-empirical MO calculations. 

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. 

Stanton, D. T., Jurs, P. C. Development and use of charged partial surface area structural descriptors in computer-assisted quantitative strucmre-property relationship smdies. Anal. Chem. 1990, 62, 2323-2329. 

Jurs and co-workers have used parameters generated by the ADAPT system [34], The descriptors fall into three categories topological, electronic, and geometric. 

Since the value of H depends on the choice of , modifications of this procedure have been proposed (Fernandez Piema and Massart 2000). Another modification of the Hopkins statistic—published in the chemometrics literature—concern the distributions of the values of the used variables (Hodes 1992 Jurs and Lawson 1991 Lawson and Jurs 1990). The Hopkins statistic has been suggested for an evaluation of variable selection methods with the aim to find a variable set (for instance, molecular descriptors) that gives distinct clustering of the objects (for instance, chemical structures)—hoping that the clusters reflect, for instance, different biological activities (Lawson and Jurs 1990). 

Besides the counts of fragments or atom types, a lot of theoretical descriptors have been successfully applied in predicting aqueous solubility. Mitchell and Jurs... 

Therefore, similar to the attempts made to estimate vapor pressure (Section 4.4) there have been a series of quite promising approaches to derive topological, geometric, and electronic molecular descriptors for prediction of aqueous activity coefficients from chemical structure (e.g., Mitchell and Jurs, 1998 Huibers and Katritzky, 1998). The advantage of such quantitative structure property relationships (QSPRs) is, of course, that they can be applied to any compound for which the structure is known. The disadvantages are that these methods require sophisticated computer software, and that they are not very transparent for the user. Furthermore, at the present stage, it remains to be seen how good the actual predictive capabilities of these QSPRs are. 

Stanton and Jurs [3] developed a model for a more diverse set of compounds, including hydrocarbons, halogenated hydrocarbons, alkanols, ethers, ketones, and esters. The model has been evaluated with 31 compounds, using, among others, charge partial surface area (CPSA) descriptors ... 

Nelson and Jurs [41] have developed models for three sets of compounds (1) hydrocarbons, (2) halogenated hydrocarbons, and (3) alcohols and ethers. Each model correlates log[C (mol L-1)] with nine molecular descriptors that represent topological, geometrical, and electronic molecule properties. The standard error for the individual models is 0.17 log unit and for a fourth model that combines all three compound sets, the standard error is 0.37 log unit. 

Among further CPSA descriptors are the differences between PPSA and PNSA, and fractional values as ratios of PPSA or PNSA and the total molecular surface area. For the original list of 25 CPSA descriptors as well as for recent extensions, the reader is referred to the literature (Stanton and Jurs, 1990 Aptula et al., 2003 Mattioni et al., 2003 Mosier et al., 2003). It should be further noted that, in contrast to the initially introduced electrostatic molecular surface interaction terms (Grigoras, 1990), the CPSA descriptors were actually defined using solvent accessible surface areas instead of simple van der Waals surface areas (Stanton and Jurs, 1990), a fact that is ignored in the present discussion for the sake of simplicity. 

The Shadow-XY fraction is a geometric descriptor related to the breadth of a molecule (Rohr-baugh and Jurs 1987). This is consistent with our observation that substitution of a bulky group at 7a and lip position of H2 increased the breadth of a chemical and enhanced ER binding (Fang et al., 2001). 

Figure 13.5 Decision Tree model. The model displays a series of yes/no (Y/N) rules to classify chemicals into active (A) and inactive (I) categories based on five descriptors Phenolic Ring Index, log K, Jurs PNSA-2, Shadow XY, and Jurs RPCS. The squares represent the rules, while the circles represent the categorical results. 

Bakken and Jurs classify three types of models. A type 1 model uses multiple regression analysis to find a linear equation involving a descriptor set. This is the type we have discussed so far—and focus on—in this chapter. A type 2 model uses neural network analysis to develop a linear/nonlinear... 

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