Descriptor interpretation

Descriptor Interpretation refers to the evaluation of molecular descriptors to derive features of their underlying chemical structure or the entire chemical structure. 

Some of the effects previously described are valuable for automatic RDF interpretation. In fact, this sensitivity is an elementary prerequisite in a rule base for descriptor interpretation. However, since many molecular properties are independent of the conformation, the sensitivity of RDF descriptors can be an undesired effect. Conformational changes occur through several effects, such as rotation, inversion, configuration interchange, or pseudo-rotation, and almost all of these effects occur more or less intensely in Cartesian RDF descriptors. If a descriptor needs to be insensitive to changes in the conformation of the molecule, bond-path descriptors or topological bond-path descriptors are more appropriate candidates. Figure 5.7 shows a comparison of the Cartesian and bond-path descriptors. 

The planned use of QSAR model predictions is an important factor to take into consideration in physico-chemical properties and biological activities prediction, and in virtual screening aimed at prioritizing and planning the design of safer alternatives. The primary focus, also in regulation, should be predictive ability verified on new chemicals, while descriptor interpretations are secondary. The order of OECD principles must be followed. A preconceived notion of what descriptors mean can be a potential source of error in SAR interpretation. Even a minute change in the compound structure can result in a substantial activity... 

We must now mention, that traditionally it is the custom, especially in chemo-metrics, for outliers to have a different definition, and even a different interpretation. Suppose that we have a fc-dimensional characteristic vector, i.e., k different molecular descriptors are used. If we imagine a fe-dimensional hyperspace, then the dataset objects will find different places. Some of them will tend to group together, while others will be allocated to more remote regions. One can by convention define a margin beyond which there starts the realm of strong outliers. "Moderate outliers stay near this margin. 

On the other hand, techniques like Principle Component Analysis (PCA) or Partial Least Squares Regression (PLS) (see Section 9.4.6) are used for transforming the descriptor set into smaller sets with higher information density. The disadvantage of such methods is that the transformed descriptors may not be directly related to single physical effects or structural features, and the derived models are thus less interpretable. 

The descriptors used should not be highly collinear with each other, for two reasons. First, this can lead to statistical instability and overprediction, and second, collinearity makes mechanistic interpretation difficult. For example, Cronin and Schultz [41] have pointed out that although a good correlation could be obtained between the skin sensitization potential and the hydrophobicity of a series of bromoalkanes, a correlation between skin sensitization potential and molecular weight had exactly the same statistics, because hydrophobicity and molecular weight are very highly correlated in homologous series. 

Our approach is to examine small, closely-related series of nitrosamines and to develop structure-activity models based on molecular descriptors which are explicitly meaningful with respect to the organic chemistry and biochemistry of the compounds. The forms of these models can then often be interpreted in terms of the mechanisms through which these compounds exert their carcinogenic effects. 

Lipophilicity is intuitively felt to be a key parameter in predicting and interpreting permeability and thus the number of types of lipophilicity systems under study has grown enormously over the years to increase the chances of finding good mimics of biomembrane models. However, the relationship between lipophilicity descriptors and the membrane permeation process is not clear. Membrane permeation is due to two main components the partition rate constant between the lipid leaflet and the aqueous environment and the flip-flop rate constant between the two lipid leaflets in the bilayer [13]. Since the flip-flop is supposed to be rate limiting in the permeation process, permeation is determined by the partition coefficient between the lipid and the aqueous phase (which can easily be determined by log D) and the flip-flop rate constant, which may or may not depend on lipophilicity and if it does so depend, on which lipophilicity scale should it be based ... 

Another possible advantage with MolSurf descriptors (and also other multi parameter descriptors) is the fact that they describe the investigated compounds not only with a single value, as in the case of PSA and log P descriptors, but in a multivariate way. This approach provides a more balanced description of the requirements that a structure must have in order to be well absorbed and may, in turn, provide additional insight on how to develop compounds having favorable absorption properties. However, as will be described in Section 16.4.10, simpler -i.e., less computationally demanding - parameters carrying similar information content with equal interpretability may be used to derive models for intestinal absorption at the same level of statistical quality. 

The interpretability of the derived NN model may be difficult to understand, even though the influences of the descriptors on the derived model can be simulated. 

In the following section, the calculation of the VolSurf parameters from GRID interaction energies will be explained and the physico-chemical relevance of these novel descriptors demonstrated by correlation with measured absorption/ distribution/metabolism/elimination (ADME) properties. The applications will be shown by correlating 3D molecular structures with Caco-2 cell permeabilities, thermodynamic solubilities and metabolic stabilities. Special emphasis will be placed on interpretation of the models by multivariate statistics, because a rational design to improve molecular properties is critically dependent on an understanding of how molecular features influence physico-chemical and ADME properties. 

The model interpretation is in good agreement with the known molecular factors influencing Caco-2 permeability. In addition - and this outlines the originality of the method - VolSurf allows the relevant 3D molecular properties to be quantified. Once the model is developed, as reported above, simple projection of the compound descriptors into it allows predictions to be made for new compounds. 

D-molecular descriptors, alignment-independent and based on molecular interaction, called GRIND have been developed. These are autocorrelation transforms that are independent of the orientation of the molecules in 3D space. The original descriptors can be extracted from the autocorrelation transform with the ALMOND program. The basic idea is to compress the information present in 3D maps into a few 2D numerical descriptors which are very simple to understand and interpret. 

As required by (36), the variational parameter k is calculated to vary between k = 2 at R = 0 and k = 1 at R > 5ao- The parameter k is routinely interpreted as either a screening constant or an effective nuclear charge, as if it had real physical meaning. In fact, it is no more than a mathematical artefact, deliberately introduced to remedy the inadequacy of hydrogenic wave functions as descriptors of electrons in molecular environments. No such parameter occurs within the Burrau [84] scheme. 

The cu-bonding model provides a more complete and fundamental description of hypervalent molecules that are often interpreted in terms of the VSEPR model.144 In the present section we examine some MX species that are commonly used to illustrate VSEPR principles, comparing and contrasting the VSEPR mnemonic with general Bent s rule, hybridization, and donor-acceptor concepts for rationalizing molecular geometry. Tables 3.32 and 3.33 summarize geometrical and NBO/NRT descriptors for a variety of normal-valent and hypervalent second-row fluorides to be discussed below, and Fig. 3.87 shows optimized structures of the hypervalent MF species (M = P, S, Cl n = 3-6). 

From the interpretation given to the Fukui function, one can note that the sign of the dual descriptor is very important to characterize the reactivity of a site within a molecule toward a nucleophilic or an electrophilic attack [29,30]. That is, if A/(r) > 0, then the site is favored for a nucleophilic attack, whereas if A/(r) < 0, then the site may be favored for an electrophilic attack. 

In this chapter, the diverse coupling constants and MEC components identified in the combined electronic-nuclear approach to equilibrium states in molecules and reactants are explored. The reactivity implications of these derivative descriptors of the interaction between the electronic and geometric aspects of the molecular structure will be commented upon within both the EP and EF perspectives. We begin this analysis with a brief survey of the basic concepts and relations of the generalized compliant description of molecular systems, which simultaneously involves the electronic and nuclear degrees-of-freedom. Illustrative numerical data of these derivative properties for selected polyatomic molecules, taken from the recent computational analysis (Nalewajski et al., 2008), will also be discussed from the point of view of their possible applications as reactivity criteria and interpreted as manifestations of the LeChatelier-Braun principle of thermodynamics (Callen, 1962). 

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