Computer Assisted Interpretation

Perhaps one way of addressing this is to use semi-empirical calculations of shifts [95, 96]. These methods make explicit use of 3D stmcture and 

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Elucidation of Structural Fragments by Computer-Assisted Interpretation of IR Spectra... 

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. 

Danis, P.O. and Huby, F.J., The Computer-Assisted Interpretation of Copolymers Mass Spectra, /. Amer. Soc. Mass Spectrom., 6, 1112 (1995). 

Wonders must not be expected from conceptually rather simple methods of pattern recognition for the solution of very complex problems (like the interpretation of a spectrum or the description of structure-activity-relationships). Pattern recognition can be seen only as one part of a computer-assisted interpretation system for chemical data. Accentuation should be given to "assisted because a complete processing by a machine of sophisticated data interpretations in chemistry is unrealistic and uneconomical at least for the next two decades. 

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. 

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. 

One of the most serious limitations in the application of these powerful GC/MS and LC/MS systems is the accurate and efficient identification of this flood of unknown mass spectra. A variety of computer-assisted techniques have been proposed (2, 3, 4), which can be classified generally as "retrieval" or "interpretive programs (2). The former matches the unknown mass spectrum against a data base of reference spectra the ultimate limitation of this approach is the size of the data base, which currently contains the mass spectra of 33,000 different compounds (j>, 6), less than 1% of the number listed by Chemical Abstracts. If a satisfactorily-matching reference spectrum cannot be found by the retrieval program, an interpretive algorithm can be used to obtain partial or complete structure information, or to aid the human interpreter in this task (7-10). 

Advantages. This is critical technology to enable both automated and routine application of computer-assisted optimization. The manual effort required for traditional approaches to data interpretation in chromatographic method development is quite considerable. 

The RDF descriptor is interpretable by using simple rule sets, and thus it provides a possibility for conversion of the code back into the corresponding 3D structure. Besides information about interatomic distances in the entire molecule, the RDF code provides further valuable information, e.g., about bond distances, ring types, planar and non-planar systems and atom types. This fact is a most valuable consideration for a computer-assisted code elucidation. 

Many early PBPK modeling efforts were based on the Simusolv software, and support for this seems not readily available at the present time. More recently the ACSL and Berkeley Madonna (University of California, Berkley, CA) have become more widely used. In addition to these computer software packages, Haddad et al. [35] demonstrated the application of a spreadsheet program to support a PBPK model, and Trent University (Peterborough, Ontario, Canada, updated 2003) made available a spreadsheet program to run PBPK models. Further there are several computer-assisted applications, several as freeware, to perform pharmacokinetic analyses and interpret in vitro enzyme kinetic data (see Chapter 3). 

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