Descriptor-based analyses

In our study we compare two diversity-driven design methods (uniform cell coverage and clustering), two analysis methods motivated by similarity (cell-based analysis and cluster-classification), and two descriptor sets (BCUT and constitutional). Thus, our study addresses some of the many questions arising in a sequential screen how to choose the initial screen, how to analyze the structure-activity data, and what molecular descriptor set to use. The study is limited to one assay and thus cannot be definitive, but it at least provides preliminary insights and reveals some trends. 

There are two separate but interrelated aspects to QSAR modeling of antibacterial peptides the choice of QSAR descriptors and the choice of numerical analysis techniques used to relate these values to antibacterial activity. A simple example of a QSAR descriptor is the total charge of a peptide. A large number of QSAR descriptors is available for small compounds in the literature and from commercial software products that may be considered. A smaller subset is used in QSAR studies of antibacterial peptides and may be separated into two categories descriptors based on empirical values and calculated descriptors. An example of an empirical value is HPLC retention time, which is a surrogate measure of solubility or hydrophilicity/hydrophobicity. An example of a calculated descriptor is total peptide charge at pH 7. 

Density Organic explosives tend to have higher density than equivalent harmless plastic materials Diffraction profile yields density descriptor based on analysis of molecular interference function... 

In the context of computational toxicology, quantum chemical descriptors provide distinct probes to unravel mechanistic causes for the hazardous effects of chemical substances. At the same time, the level of theory employed may be crucial for the molecular property under analysis, which is particularly true for descriptors based on net atomic charges (that, in turn, are not physically observable, despite their intuitive meaning for charge-controlled intermolecular interactions). 

Solubility is a key property in the distribution of the compound from the gastrointestinal tract to the blood. There have been several modeling efforts to predict the solubility, based on different type of descriptors. The intrinsic solubility (thermodynamic solubility of the neutral species) for a set of 1028 compounds has been modeled using the VolSurf descriptors based on GRID-MIFs (Fig. 10.9(a)) and PLS multivariate analysis [20]. The interpretation of the model can be based on the PLS coefficients the ratio of the surface that has an attractive interaction with the water probe contributes positively to the solubility, while the hydrophobic interactions and log P have a negative contribution. 

Descriptors based on the 2D structure or simply on the connectivity matrix of a structure have long been used for chemical similarity and for property correlations. Because they often lack any relationship to mechanism, these descriptors are best used within a congeneric series or at least a set of similar structures. They may be empirically useful for cluster analysis and chemical library design, because they are effective at representing structure differences and similarities. A few programs and providers of topological descriptors include the following ... 

By calculating molecular descriptors based on 3D geometry without a common orientation frame, the Comparative Molecular Moment Analysis overcomes the problems due to the molecular alignment. 

Several different kinds of quantum-chemical descriptors have been defined and these can be broadly divided into energy-based descriptors, orbital energies descriptors, local quantum-chemical properties, descriptors based on the analysis of the wave function, frontier orbital electron densities, superdelocalizability indices, polarizabilities, and derived from the Density Functional Theory [Cartier and Rivail, 1987 Bergmarm and Hinze, 1996 Karelson, Lobanov et al., 1996]. 

Another descriptor based on eigenvalues is the EVA descriptor [66,67] (where EVA stands for EigenVAlue). The EVA descriptor is an example of a 3D descriptor that does not require alignment of the 3D structures (i.e., it is invariant to translation or rotation of the structure). It is based on the infrared spectrum of a molecule, which is related to the 3D structure. The molecular vibrations are calculated using a normal coordinate analysis (NCA) of the energy-minimized structure (e.g., using MOPAC AMI), and the eigenvalues from the NCA are used to derive the EVA descriptor (see Ref. 67). 

Other whole molecule descriptors that do not require alignment include the Weighted Holistic Invariant Molecular (WHIM) indices developed by Todeschini et al. [70]. These indices are calculated from the 3D coordinates, which are weighted and centered to make them invariant to translation principal component analysis (PCA) is applied to obtain three principal components. These are used to produce new coordinates, which can be analyzed to obtain a series of 10 descriptors based on eigenvalues and the third-order and fourth-order moments of the three score column vectors. These descriptors are related to molecular size, shape, symmetry, and atom distribution and density. 

The more subtle, but potentially more exciting, consequence of such multidimensional data analysis is the superposition of biological annotations onto a collection of measurements, allowing connections between the biological coordinates to be made independently of the measurements themselves. As we have seen in this chapter, there are specific relationships between various calculated molecular descriptors, based on the theory of their construction or on their relationships to molecular properties such as size and shape. Similarly, there are implicit encodable relationships between the different assays that comprise any multidimensional fingerprint of assay outcomes, such as combinations of cell states and cellular assays (Fig. 13.1-7(c)). Exploiting such relationships across diverse collections of small molecules indeed may uncover new relationships between the biological states themselves. 

Since a conventional TEM produces a projected three-dimensional (3D) image on a two-dimensional (2D) plane, quantitative information on the filler dispersion can be attained by using AIA techniques. 97,99,102,115,125,127,134 this way, it is possible to recognize, select, measure and compare size and shape of the complex structures dispersed in the matrix through the use of descriptors based on geometrical parameters, such as area, perimeter, diameter and morphometric parameters, such as shape ratio and roundness (Figure 23.3). However, TEM image analysis of filler microdispersions is more difficult to... 

Very recent and quite independent support for this comes from a study of Pogliani and Julian-Ortiz on QSPR with descriptors based on averages of vertex invariants [107]. They introduced a new type of indices, the mean molecular connectivity indices, based on a number of different concepts of mean. They have been interested in comparative study of multiple regression analysis (MRA) and artificial neural network (AAN) approaches. For illustration, they examined a selection of physicochemical properties of organic solvents, using the mean molecular connectivity... 

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