Structure-activity methods pattern recognition

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. 

Dunn III, W.J., Wold, S. and Martin, Y.C (1978). Structure-Activity Study of p-Adrenergic Agents Using the SIMCA Method of Pattern Recognition. J.Med.Chem., 21,922-930. 

Applications of pattern recognition methodology to chemical problems were first reported in the 1960 s (20,21) with studies of mass spectra. Since then papers have described work in a variety of areas (22,23) including mass spectrometry, infrared spectroscopy, NMR spectroscopy, electrochemistry, materials science and mixture analysis, and the modeling of chemical experiments. Diagnosis of pathological conditions from sets of measurements made on complex biological mixtures, e.g., serum, have been reported (24). The successes in these areas have led to the belief that these methods should prove useful in the development of structure-activity relations. 

J. M.G., Marin, P.N., Crespo-Otero, R., Zaragoza, E.T. and Garci a-Domenech, R. (2007) Applying pattern recognition methods plus quantum and physico-chemical molecular descriptors to analyze the anabolic activity of structurally diverse steroids. J. Comput. Chem., 29, 317-333. 

Vendrame, R., Braga, R.S., Takahata, Y. and Galvao, D. S. (2001) Structure-cardnogenic activity relationship studies of polycyclic aromatic hydrocarbons (PAHs) with pattern-recognition methods./. Mol. Struct. (Theochem), 539,252—265. 

Pattern recognition techniques can be divided into display, preprocessing, supervised, and unsupervised learning. Pattern recognition methods are used among others in the search for correlations between sequence, structure, and biological activity in cheminformatics and bioinformatics. 

A relation can be discovered between the structure and activity by applying statistical and pattern recognition methods to a set of tested compounds. 

Flow chart of steps involved in structure-activity studies using chemical structure information, handling and pattern recognition methods... 

ADAPT has a variety of pattern recognition and statistical methods available for use. The object of the analysis phase is to find discriminants that separate subsets of the data into the proper categories. That is, one is trying to find mathematical models that will classify compounds as belonging to the active or inactive subset based on the molecular structure descriptors available. This phase of SAR studies Is guided by the user in a highly interactive manner in order to search through the available descriptors for the best set. 

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. 

Chapter 12 gives an overview about pattern recognition applications in chemistry. Chapters 13 to 20 extensively describe applications in spectral analysis, chromatography, electrochemistry, material classification, Structure-activity-relationship research, clinical chemistry, environmental chemistry and classification of analytical methods. 

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