Molecular modeling similarity searching

Molecular databases and the associated data banks require the development of a conceptual structure for the information stored about the molecules, descriptive language representing the data, and methods for analysis enabling molecular modeling, similarity searches, classification, visualization, or other uses of the database.320 Currendy, the Protein Data Bank (PDB http 7www.rcsb.org/pdb/) is one of the best known examples of a molecular database. The PDB is a worldwide archive of three-dimensional structural data of biological macromolecules.321 The PDB is a common accentor to many structural databases.322 The success of... 

A QSAR approach based on a set of methods that combines molecular shape similarity and commonality measures with other - molecular descriptors both to search for similarities among molecules and to build QSAR models [Hopfinger, 1980 Burke and Hopfinger, 1993], The term molecular shape similarity refers to molecular similarity on the basis of a comparison of three-dimensional molecular shapes represented by some property of the atoms composing the molecule, such as the van der Waals spheres. TTie molecular shape commonality is the measure of molecular similarity when conformational energy and molecular shape are simultaneously considered [Hopfinger and Burke, 1990]. 

Different physical properties and molecular models have been used to define the molecular surface the most common are reported below together with the descriptors proposed as measures of surface areas and molecular volume (- volume descriptors). Molecular surface area and volume are parameters of molecules that are very important in understanding their structure and chemical behaviour such as their ability to bind ligands and other molecules. An analysis of molecular surface shape is also an important tool in QSAR and - drug design-, in particular, both - molecular shape analysis and - Mezey 3D shape analysis were developed to search for similarities among molecules, based on their molecular shape. 

Dean, P.M. and Perkins, T.DJ. (1993). Searching for Molecular Similarity Between Flexible Molecules. In Trends in QSAR and Molecular Modelling 92 (Wermuth, C.G., ed.), ESCOM, Leiden (The Netherlands), pp. 207-215. 

Abstract The aim of the present chapter is to present the current research and potential applications of chemoinformatics tools in food chemistry. First, the importance and variety of molecular descriptors and physicochemical properties is delineated, and then a survey and chemical space analysis of representative databases with emphasis on food-related ones is presented. A brief description of methods commonly used in molecular design, followed by examples in food chemistry are presented, such methods include similarity searching, pharmacophore modeling, quantitative... 

Once a first electron-density map is obtained, it is interpreted by the crystallographer. In the case of a MIR(AS) map, a complete model of the protein has to be fitted to the electron density. The Ca atoms are placed first (chain tracing), and subsequently the complete main chain and the side chains are built, a process which has become more and more automated in recent years, notably when high resolution data are available. In the case of molecular replacement, the search model needs to be updated to reflect the molecule present in the crystal. The model is usually of a similar protein and the changes involve the substitution of some amino acids, the introduction of insertions and deletions, the modification of some loops, and so on. 

RDF descriptors exhibit a series of unique properties that correlate well with the similarity of structure models. Thus, it would be possible to retrieve a similar molecular model from a descriptor database by selecting the most similar descriptor. It sounds strange to use again a database retrieval method to elucidate the structure, and the question lies at hand Why not directly use an infrared spectra database The answer is simple. Spectral library identification is extremely limited with respect to about 28 million chemical compounds reported in the literature and only about 150,000 spectra available in the largest commercial database. However, in most cases scientists work in a well-defined area of structural chemistry. Structure identification can then be restricted to special databases that already exist. The advantage of the prediction of a descriptor and a subsequent search in a descriptor database is that we can enhance the descriptor database easily with any arbitrary compound, whether or not a corresponding spectrum exists. Thus, the structure space can be enhanced arbitrarily, or extrapolated, whereas the spectrum space is limited. 

Wang, Y. and Bajorath, J. (2008) Balandng the influence of molecular complexity on fingerprint similarity searching. /. Chem. Inf. Model., 48, 75-84. 

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