Computed assisted spectral

The experiment is used to separate chemical shifts and J-couplings for homo- and heteronudear systems. In simple cases the chemical shifts and J-couplings may be directly obtained from the 2D spectrum by inspection. For severely overlapped first-order spectra or strongly coupled spin systems the estimated parameters obtained from the spectrum may be used as starting values in a computer assisted spectral analysis as outlined in Modern Spectral Analysis (Volume 3). 

An IR technique has been used to obtain spectra of a reacting PU foam system having a given temperature profile every 5-10 seconds. Chemical reaction rates and morphology development were evaluated using computer assisted spectral analysis to simultaneously monitor both isocyanate disappearance and the evolution of urethane and urea carbonyl absorbancies. Quantitation of the products vs. time is obtained via curve fitting and deconvolution routines. Applications have been proven in a wide variety of cellular PU systems. 15 refs. 

After approaches to the solution of the major tasks in chemoinformatics have thus been outlined, these methods are put to work in specific applications. Some of these apphcations, such as structure elucidation on the basis of spectral information, reaction prediction, computer-assisted synthesis design or drug design, are presented in Chapter 10. 

However, better use of spectral information for more rapid elucidation of the structure of a reaction product, or of a natural product that has just been isolated, requires the use of computer-assisted structure elucidation (CASE) systems. The CASE systems that exist now are far away from being routinely used by the bench chemist. More work has to go into their development. 

P.J. Lewi, Spectral mapping of drug-test specificities. In Advanced Computer-Assisted Techniques in Drug-Discovery (H. van de Waterbeemd, Ed.). VCH, Weinheim, Germany, 1994, pp. 219-253. 

Blaffert T (1984) Computer-assisted multicomponent spectral analysis with fuzzy data sets. Anal Chim Acta 161 135... 

N.A.B. Gray, A. Buchs, D.H. Smith and C. Djerassi, Application of artificial intelligence for chemical inference. Part XXXVI. Computer assisted structural interpretation of mass spectral data, Helv. Chim. Acta, 64 (1981) 458-470. 

The comparison can be made manually on the basis of collections of tables (for example, A. Cornu R. Massot Compilation of Mass Spectral Data) or may be effected with computer assistance large databases can be used (e.g. Mass Spectral Data Base, Royal Society of Chemistry, Cambridge). 

The past two decades have seen a marked change in the reporting of the application of various techniques to the determination of structure. Most papers relating to the synthesis of organic molecules now confine comments on UV and IR data to a minimum. While NMR data are presented in more detail, much 13C NMR spectral information is often presented simply as a catalogue of chemical shifts with little or no attempt at assignment. X-Ray structural determinations have become more commonplace and many papers now contain ORTEP representations of molecules. In addition, proposed structures are frequently supported by calculations and computer-assisted representations. 

WA Warr. Computer assisted structure elucidation. Library search and spectral data collections. Anal Chem 65 1045A-1050A, 1993. 

W.A. Warr, Computer-assisted Structure Elucidation - Library Search and Spectral Data Collections, Analytical Chcniisliy. 65 (1993), 1045A-1050A. 

The chiroptical properties of optically active thiazoUdines derived from aldoses and natural mercapto aminoacids was studied [92]. PMR parameters for thiazoHdine-4(R)-carboxylate derivatives were obtained by computer-assisted analysis of their spectra. The polyacetoxy-alkyl side chains have planar zig-zag conformations. The configurations at C-2 in the di-astereoisomers were ascertained on the basis of the Jnh.ch coupling constants [93]. The conformation and stereochemistry of diastereomeric sulfoxides of methyl 3-acetyl-5,5-dimethyl-2-(D-galactopentaacetoxypentyl)-1,3-thiazolidine-4-carboxylate 1-oxides were performed by H- and C-NMR spectral analysis [94]. 

Intelligent computer assisted interpretation of spectroscopic data should be based on the knowledge from large structure oriented data collections. Both the inspection of spectral features and the statistical evaluation of similar structures (from library searches) can provide a set of probability ranked substructures which are readily assembled to target structures. The idea of substructure analysis allows the chemist to combine the results of different interpretation strategies, different databases and different spectroscopic methods to yield the structural information desired. Thus in a multidimensional data system hke SPECINFO structural noise can be effectively suppressed, if all information available in the spectroscopic laboratory is combined in a central intelligent computer system. 

Three different approaches have been used for computer-assisted interpretations of chemical data. 1. Heuristic methods try to formulate computer programs working in a similar way as a chemist would solve the problem. 2. Retrieval methods have been successfully used for library search (an unknown spectrum is compared with a spectral library). 3. Pattern recognition methods are especially useful for the classification of objects (substances, materials) into discrete classes on the basis of measured features. A set of characteristic features (e.g. a spectrum) of an object is considered as an abstract pattern that contains information about a not directly measurable property (e.g. molecular structure or biological activity) of the object. Pure pattern recognition methods try to find relationships between the pattern and the "obscure property" without using chemical knowledge or chemical prejudices. 

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