Structural descriptors, examples

Further prerequisites depend on the chemical problem to be solved. Some chemical effects have an undesired influence on the structure descriptor if the experimental data to be processed do not account for them. A typical example is the conformational flexibility of a molecule, which has a profound influence on a 3D descriptor based on Cartesian coordinates. In particular, for the application of structure descriptors with structure-spectrum correlation problems in... 

With the development of accurate computational methods for generating 3D conformations of chemical structures, QSAR approaches that employ 3D descriptors have been developed to address the problems of 2D QSAR techniques, that is, their inability to distinguish stereoisomers. Examples of 3D QSAR include molecular shape analysis (MSA) [26], distance geometry,and Voronoi techniques [27]. The MSA method utilizes shape descriptors and MLR analysis, whereas the other two approaches apply atomic refractivity as structural descriptor and the solution of mathematical inequalities to obtain the quantitative relationships. These methods have been applied to study structure-activity relationships of many data sets by Hopfinger and Crippen, respectively. Perhaps the most popular example of the 3D QSAR is the com-... 

For larger aggregations, a set of structural descriptors (see Table 4.7) is used. Homonuclear entities can have relatively simple names using these descriptors. The examples below give an indication of how names are arrived at. For more complex cases, the reader is referred to the Nomenclature of Inorganic Chemistry, p. 192. All the devices already discussed above can be called into use as necessary. 

Many different methods have been developed both to measure diversity and to select diverse sets of compounds, however, currently there is no clear picture of which methods are best. To date, some work has been done on comparing the various methods however, there is a great need for more validation studies to be performed both on the structural descriptors used and on the different compound selection strategies that have been devised. In some cases, the characteristics of the library itself might determine the choice of descriptors and the compound selection methods that can be applied. For example, computationally expensive methods such as 3D pharmacophore methods are limited in the size of libraries that can be handled. Thus for product-based selection, they are currently restricted to handling libraries of tens of thousands of compounds rather than the millions that can be handled using 2D based descriptors. 

The next sections describe several specific examples of how the full range of computational methods have been used to design libraries, ranging from methods using only 2-D structural descriptors, to complete structure-based combinatorial design. 

Whereas hard filters can be considered to be knowledge-driven, soft filters are the result of a data-driven approach. A quantitative structure-activity or structure-property relationship (QSAR/QSPR) is established to predict a property from a set of molecular descriptors. Examples are the above-mentioned in-silico prediction tools for frequent hitters [27] and drug-likeness [41,42] additional models for ADM E properties are described below. 

Theses are calculated from the 3D molecular structure, which is defined with coordinates of all atoms in the molecule. The step from 2D to 3D description of molecules is the crucial one [40-42]. The 3D structure is an ambiguously defined quantity, which depends on molecular environment, i.e., it is different in crystal structure, in solutions, or in vacuo. If it is theoretically determined it depends on the computational method. However, the 3D structures form a basis for a broad range of descriptors. Examples are mass distribution descriptors such as moment of inertia or gravitation index, and shape indices such as shadow indices, surface area indices, and van der Waals indices [29,40,41]. 

The estimations Equations (6.17a, b) of probabilities P Ak), P Ak D not only increase the algorithm s prediction accuracy but also open up new possibilities. For example, function fn A in the range [0,1] can be considered as a measure of molecule n belonging to a fuzzy set of molecules that reveal activity Ak- The descriptor weight gn Dd can be considered in the same manner, and then the molecule structure descriptors can be of arbitrary nature, e.g., such as in the refs. 51 and 52. 

Diversity The unrelatcdncss" of a set of. for example, building blocks or numbers of a combinatorial library. Measured using physicochemical or structural descriptors, a. set with high diversity. spans a larger fraction of "chemical space." Cluster analysis is one technique used to quantify diversity. 

Examples 3 and 4 include the structural descriptors triangulo and quadro which are introduced below in Section IR-9.2.5.7.) Note that the name in Example 3 does not specify which chloride ligands bind to which central atoms. 

Among the advantages with 2D-based descriptors are their rapid speed of computation for large sets of compounds and that they do not require 3D structures. Thus, these descriptors avoid the problem and compute times associated with 3D structure generation and conformational analysis, even though there are programs available that generate reliable 3D structures, for example, CORINA [5]. 

Let us summarize the three important prerequisites for a 3D structure descriptor It should be (1) independent of the number of atoms, that is, the size of a molecule (2) unambiguous regarding the three-dimensional arrangement of the atoms and (3) invariant against translation and rotation of the entire molecule. Further prerequisites depend on the chemical problem to be solved. Some chemical effects may have an undesired influence on the structure descriptor if the experimental data to be processed do not account for them. A typical example is the conformational flexibility of a molecule, which has a profound influence on a 3D descriptor based on Cartesian coordinates. The application in the field of structure-spectrum correlation problems in vibrational spectroscopy requires that a descriptor contains physicochemical information related to vibration states. In addition, it would be helpful to gain the complete 3D structure from the descriptor or at least structural information (descriptor decoding). 

This representation is independent of any knowledge concerning the molecular structure, and hence molecular descriptors obtained from the chemical formula can be called OD descriptors. Examples are the atom number A, molecular weight MW, atom-type count Nx, and, in general, constitutional descriptors and any function of the atomic... 

Exports of structural descriptors, SMILES and InChl, provide chemical structure information in a simple tab-delimited text file containing CID or SID and either the isomeric SMILES or InChl strings. Given the very nature of the formats of SMILES and InChl, not all chemical structure information can be identically represented. For example, SMILES encodes only covalent bonds, while PubChem supports the additional concepts of ionic, complex, and dative bonds. Most small molecules in PubChem can be reproducibly interconverted between InChl, SMILES, and PubChem ASN.l formats without loss of chemical structure information. 

The structure descriptors are not intercorrelated to allow the recognition of the significant variables. If, for example, activity data on a set of chlorophenols are investigated with respect to their log and values, a statistically significant QSAR may be obtained with either one of the descriptors, but an assignment of the relevant descriptor is not feasible, because those are linearly intercorrelated. 

Only within these limits of the descriptor values can the relationship be assumed to hold. Beyond this domain, the model may reveal a different type of relationship of the activity on the structural descriptors, which evidently must result in erroneous predictions. A striking example of the pitfalls of inapt extrapolations can be demonstrated with the application of a fourth-order polynominal log BCF/log QSAR (Connell and Hawker, 1988) outside its parameter range (log 2.6-9.8). Because of the curvature of the model, higher BCF estimates result for compounds with log P = 0 than for... 

Several subgroups of chemicals that fall into the general class of polar, nonspecific toxicants were found to be more toxic than calculated from the respective QSARs (Table 5.4) because they exerted additional specific effects. These outliers from the polar non-specific QSARs comprise, for example, phenols and anilines with < 6.3, > two NO2 substituents and/or > three halogen substituents (Bradbury et al, 1989 Nendza and Seydel, 1990 OECD 1992a). They may act as uncouplers of the oxidative phosphorylation with diverging physiological syndromes and hence their QSARs must use different structural descriptors because their toxicity is caused by different chemical properties. 

Table 1 Structural descriptors Descriptor group Typical examples Characteristics...

Table 1 Structural descriptors—cont d Descriptor group Typical examples...

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