Chemical structures, generating

The DENDRAL project initiated in 1964 at Stanford was the prototypical application of artificial intelligence techniques - or what was understood at that time under this name - to chemical problems. Chemical structure generators were developed and information from mass spectra was used to prune the chemical graphs in order to derive the chemical structure associated with a certain mass spectrum. 

Horvath, A. L., Molecular Design. Chemical Structure Generation from the Properties of Pure Organic Compounds, 1992, Amsterdam Elsevier B.V. 

Horvath AL. Molecular design Chemical structure generation from the properties of pure organic compounds. Amsterdam Elsevier, 1992. 

The design motif for this book cover consists of some examples of molecules discussed in the chapters floating above a representation of water, land, and sky. The artwork was provided by David H. Lipnick, a student in the School of the Arts at Virginia Commonwealth University, and the three-dimensional chemical structures generated by Robert L. Lipnick. 

Generate the data from the method of contributing groups which requires knowledge of the chemical structure and some careful attention. 

Chemists have been used to drawing chemical structures for more than a hundred years. Nowadays, structures are not only drawn on papei but they are also available in electronic form on a computer for publications, for presentations, or for the input and outptit with computer programs. For these applications, well-known software such as ISIS/Draw (MDL [31] or ChemWindow (Bio-Rad Sadtier [32]) arc used (see Section 2,12), The structures generated with these programs arc... 

The drawing software comprises a comprehensive collection of standard tools to sketch 2D chemical structures. To specify all its facilities and tools would go far beyond the scope of this overview, but there are some nice features that are very useful for chemists so they are mentioned here briefly. One of these enables the prediction of H and NMR shifts from structures and the correlation of atoms with NMR peaks (Figure 2-127). lUPAC standard names can be generated... 

While the trivial and trade nomenclature in most cases has accidental character, the lUPAC Commission has worked out a series of rules [4] which allow the great majority of structures to be represented uniformly, though there still exists some ambiguity within this nomenclature. Thus, many structures can have more than one name. It is important that the rules of some dialects of the lUPAC systematic nomenclature are transformed into a program code. Thus, programs for generating the names from chemical structures, and vice versa (structures from names) have been created [5] (see Chapter II, Section 2 in the Handbook). 

A most important task in the handling of molecular data is the evaluation of "hidden information in large chemical data sets. One of the differences between data mining techniques and conventional database queries is the generation of new data that are used subsequently to characterize molecular features in a more general way. Generally, it is not possible to hold all the potentially important information in a data set of chemical structures. Thus, the extraction of relevant information and the production of reliable secondary information are important topics. 

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. 

A R, D P Dolata and K Prout 1990. Automated Conformational Analysis and Structure Generation Algorithms for Molecular Perception. Journal of Chemical Information and Computer Science 30 316-324. 

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. 

Two-Dimensional Representation of Chemical Structures. The lUPAC standardization of organic nomenclature allows automatic translation of a chemical s name into its chemical stmcture, or, conversely, the naming of a compound based on its stmcture. The chemical formula for a compound can be translated into its stmcture once a set of semantic rules for representation are estabUshed (26). The semantic rules and their appHcation have been described (27,28). The inverse problem, generating correct names from chemical stmctures, has been addressed (28) and explored for the specific case of naming condensed benzenoid hydrocarbons (29,30). 

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, e.g., their inability to distinguish stereoisomers. The examples of 3D QSAR include molecular shape analysis (MSA) [34], distance geometry [35,36], and Voronoi techniques [37]. 

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