Molecular similarity analysis searching

Fig. 4.15 Hit list from Feature Tree fragment space searches are grouped by a molecular framework analysis (y-axis) and the Feature Tree similarity score (x-axis). The search algorithm indeed produces compounds from...

Similarity Searching (BLAST) Nucleotide Sequence Analysis Protein Sequence Analysis Molecular Structure Analysis t Genome Analysis Gene Expression... 

These calculations generally start with a series of known active compounds that are added as search templates (or bait molecules) to a source database and compounds that are identified as similar to these templates based on VS calculations are selected as candidate molecules for experimental evaluation. Activity-oriented VS typically aims at identifying compounds that structurally differ from known templates but have similar activity. This is often done because known active compounds are difficult to develop, not easily chemically accessible (for examples, natural products), or already covered by other patents. In such cases, VS analysis attempts to identify molecular similarity relationships that balance structural and biological similarity in rather different ways, which is often a non-trivial task. 

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. 

Several shape descriptors are defined within more general approaches to - molecular descriptors. This is the case of - Kier shape descriptors, -> shape profiles, -> shadow indices, -> WHIM shape descriptors, - Sterimol shape parameters L/Bj and B1/B5, molecular - periphery codes, and -> centric indices. Other approaches to the study of molecular surface and shape are Mezey 3D shape analysis and Hopfinger - molecular shape analysis. -> Triangular descriptors have also been used to characterize molecular shape to search for similarities among molecules. 

The two main applications of molecular descriptors have been for screens in substructure searching [4] and for attributes in similarity calculations [5]. Historically, many descriptors were developed first for substructure searching (see Chapter 19 of this book) and then used in similarity calculations for applications such as similarity analysis, clustering, combinatorial library design, and data mining (see Chapters 14,... 

The concept of molecular dissimilarity is introduced, and shown to be a powerful complement to the well-established notion of molecular similarity. It provides a quantitative assessment of structural variation and diversity. Apphcations within chemical information systems are discussed. These include ranking of search output, selection of representative sets of structures, file screening, data analysis, and creativity stimulation. 

Comparative Molecular Field Analysis (CoMFA) Conformational Flexibility in 3D Structure Searching Conformational Search Medium-sized Molecules Conformational Search Proteins Genetic and Evolutionary Algorithms Molecular Docking and Structure-based Design Shape Analysis Structural Similarity Measures for Database Searching Structure and Substructure Searching Structure Databases. 

Just as field-based methods in 3D similarity extend and complement the atom/distance-based methods, there are a variety of field-based 3D QSAR methods which complement pharmacophore searching. The most common of these is the CoMFA (comparative molecular field analysis) technique (and related methods). These methods have been applied to... 

Multivariate data analysis usually starts with generating a set of spectra and the corresponding chemical structures as a result of a spectrum similarity search in a spectrum database. The peak data are transformed into a set of spectral features and the chemical structures are encoded into molecular descriptors [80]. A spectral feature is a property that can be automatically computed from a mass spectrum. Typical spectral features are the peak intensity at a particular mass/charge value, or logarithmic intensity ratios. The goal of transformation of peak data into spectral features is to obtain descriptors of spectral properties that are more suitable than the original peak list data. 

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