Descriptor comparison

We have also made similar descriptor comparisons of compounds from the Merck chemical collection, the top 200 selling drugs in 2006, and Merck s natural product collection. 

A key question is as follows Can SE and DSE, as an information theoretic approach to descriptor comparison and selection, be applied to accurately classify compoimds or to model physiochemical properties To answer this question, two conceptually different applications of SE and DSE analysis will be discussed here and related to other studies. The first application explores systematic differences between compound sets from synthetic and natural sources." The second addresses the problem of rational descriptor selection to predict the aqueous solubility of synthetic compounds." For these purposes, SE or DSE analysis were carried out, and in both cases, selected descriptors were used to build binary QSAR-like classification models. 

When a structure is input for spectra simulation this structuie is also coded as an RDF descriptor, which allows an easy comparison with the structures in the database, Those 50 stnictures which are most similai to the input structui e are then selected together with their spectra. 

B and W J Howe 1991. Computer Design of Bioactive Molecules - A Method for Receptor-Based Novo Ligand Design. Proteins Structure, Function and Genetics 11 314-328. i H L 1965. The Generation of a Unique Machine Description for Chemical Structures - A hnique Developed at Chemical Abstracts Service. Journal of Chemical Documentation 5 107-113. J 1995. Computer-aided Estimation of Symthetic Accessibility. PhD thesis. University of Leeds, itan R, N Bauman, J S Dixon and R Venkataraghavan 1987. Topological Torsion A New )lecular Descriptor for SAR Applications. Comparison with Other Descriptors. Journal of emical Information and Computer Science 27 82-85. 

In the Fischer convention, the ermfigurations of other molecules are described by the descriptors d and L, which are assigned comparison with the reference molecule glyceraldehyde. In ertqrloying the Fischer convention, it is convenient to use projection formulas. These are planar representations defined in such a w as to convey three-dimensional structural information. The molecule is oriented with the major carbon chain aligned vertically in such a marmer that the most oxidized terminal carbon is at the top. The vertical bonds at each carbon are directed back, away fiom the viewer, and the horizontal bonds are directed toward the viewer. The D and L forms of glyceraldehyde are shown below with the equivalent Fischer projection formulas. 

Filimonov DA, Poroikov VV, Borodina Y, Gloriozova T. Chemical similarity assessment through multilevel neighborhoods of atoms definition and comparison with the other descriptors. J Chem Inf Comput Sci 1999 39 666-70. 

Aqueous solubility is selected to demonstrate the E-state application in QSPR studies. Huuskonen et al. modeled the aqueous solubihty of 734 diverse organic compounds with multiple linear regression (MLR) and artificial neural network (ANN) approaches [27]. The set of structural descriptors comprised 31 E-state atomic indices, and three indicator variables for pyridine, ahphatic hydrocarbons and aromatic hydrocarbons, respectively. The dataset of734 chemicals was divided into a training set ( =675), a vahdation set (n=38) and a test set (n=21). A comparison of the MLR results (training, r =0.94, s=0.58 vahdation r =0.84, s=0.67 test, r =0.80, s=0.87) and the ANN results (training, r =0.96, s=0.51 vahdation r =0.85, s=0.62 tesL r =0.84, s=0.75) indicates a smah improvement for the neural network model with five hidden neurons. These QSPR models may be used for a fast and rehable computahon of the aqueous solubihty for diverse orgarhc compounds. 

D., Balahan, A. T. Comparison of weighting schemes for molecular graph descriptors application in quantitative structure-retention relationship models for alkylphenols in gas-liquid chromatography. J. Chem. Inf. Comput. Sci. 2000, 40, ITl-lM,. 

Zissimos, A. M., Abraham, M. H., Klamt, A., Eckert, F., Wood J. A comparison between the two general sets of linear free energy descriptors of Abraham and Klamt. J. Chem. Inf. Comput. Set. 2002, 42, 1320-1331. 

Similarity Comparison of molecules using molecular descriptors and a measure of similarity, for example a 2D fingerprint and the Tanimoto coefficient... 

The coefficients of descriptors A, AB and S are not statistically significant, but the terms are left in the equation for comparison with Abraham and Le s [8] water-solubility equation, modified here to conform to the mathematical conventions used in this chapter ... 

How well can continuum solvation models distinguish changes in one or another of these solvent properties This is illustrated in Table 2, which compares solvation energies for three representative solutes in eight test solvents. Three of the test solvents are those shown in Table 1, one is water, and the other four were selected to provide useful comparisons on the basis of their solvent descriptors, which are shown in Table 3. Notice that all four solvents in Table 3 have no acidity, which makes them more suitable, in this respect, than 1-octanol or chloroform for modeling biomembranes. Table 2 shows that the SM5.2R model, with gas-phase geometries and semiempirical molecular orbital theory for the wave function, does very well indeed in reproducing all the trends in the data. 

Optimized structures of these species are shown in Fig. 4.31 and geometrical and NBO descriptors are summarized in Table 4.21. As shown by comparisons with Table 4.19, the geometrical features of the nitride and imide complexes exhibit... 

For comparison, let us also consider the ten-electron M(ligand)+ complexes for M+ = Au+ as a prototype one-pair-acceptor species. (For convenience, we refer to these species as formal Au+ complexes, but it should be understood that the actual bonding is far more covalent than the ionic label Au+ might seem to suggest.) Optimized structures for the AuL2+ complexes are shown in Fig. 4.87 for direct comparison with the Ir+ complexes in Fig. 4.86. Similarly, the energetic, geometrical, and NBO descriptors of these Au+ complexes are summarized in... 

On the other hand, there is considerable interest to quantify the similarities between different molecules, in particular, in pharmacology [7], For instance, the search for a new drug may include a comparative analysis of an active molecule with a large molecular library by using combinatorial chemistry. A computational comparison based on the similarity of empirical data (structural parameters, molecular surfaces, thermodynamical data, etc.) is often used as a prescreening. Because the DFT reactivity descriptors measure intrinsic properties of a molecular moiety, they are in fact chemical fingerprints of molecules. These descriptors establish a useful scale of similarity between the members of a large molecular family (see in particular Chapter 15) [18-21],... 

The quantitative comparison of the optimized 3D structure of a selected set of ligands allows the development of their minimal 3D structural requirements for the recognition and activation of the biological target, that is, the pharmacophore hypothesis, and gives a sound 3D rationale to the available SARs [21]. A more complete and mechanistically relevant approach to the development of the 3D pharmacophore consists in its translation into a numerical molecular descriptor that quantifies the molecular-pharmacophore similarity-diversity for computational QSAR modeling [21,41]. 

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