Compound classification and selection

The classification of molecular data sets according to pre-defined criteria is one of the central themes in chemoinformatics. Methods designed to classify molecules are 

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The Morgan Algorithm classifies all the congeneric atoms of a compound and selects invariant-labeled atoms (see Section 2.5.3.1). The classification uses the concept of considering the number of neighbors of an atom (connectivity), and does so in an iterative manner (extended connectivity, EC). On the basis of certain rules. 

In the previous paragraphs a brief account has been given of the fundamental aspects of the crystallographic description of the structures and structure types of solid phases. A number of symbols and names have been defined and their application to intermetallic compounds exemplified. It must, however, be underlined that both for historical reasons and for the need to improve classification and interpretation of the structural characteristics of intermetallic phases, other symbols and nomenclature criteria have been invented. Some of them have a mathematical basis, others are more colloquial. A selection of these criteria will be given in the following. 

Abstract This chapter reviews chemical structures of biologically active, volatile compounds in beetles. Techniques used for structure elucidation are briefly discussed as well as facts and speculations on the biosynthesis of target compounds. Syntheses of selected substances are cursorily presented. The order of sections follows taxonomic classifications. Depending on the biological significance of relevant compounds in certain taxa, the corresponding sections are again subdivided into attractive compounds (mostly intraspecifically active pheromones) and defensive compounds (mostly interspecifically active allomones). 

Bajorath J (2001) Selected concepts and investigations in compound classification, molecular descriptor analysis and virtual screening. J Chem Inf Comput Sci 41 233... 

Permeability of compounds across a cell layer is measured in order to determine the absorption potential of a compound or a chemical series and select compounds for in vivo studies. Apparent permeability coefficients can be used to compare compounds within a series for ranking. Between different laboratories comparison of compounds should be done only on the basis of classification (high, medium, low) since permeability coefficients can differ between the labs (Artursson et al. 2001 see Critical Assessment of the Method). 

Chapter 6 reveals how we are going to subdivide organic chemistry. We shall use a mechanistic classification rather than a structural classification and explain one type of reaction rather than one type of compound in each chapter. In the rest of the book most of the chapters describe types of reaction in a mechanistic way. Here is a selection. 

In CombC, it is common to apply HTS in order to find compounds with a specific activity, e.g., compounds which are selective enzyme inhibitors. The HTS is then run only to trace active compounds (not selective) by just giving an active/inactive classification against the target enzyme. However, if one is to find and pursue any opportunities to optimise the selectivity, it is necessary to measure on a wider spectrum of fast screening models. 

In step 1, the initial compounds are usually selected at random from the data set. Random selection is quick and, for large heterogeneous data sets, likely to provide a reasonable initial set. Steps 2 and 3 can be performed separately or in combination. If done separately, the classification (step 2) is performed on... 

Simplistic and heuristic similarity-based approaches can hardly produce as good predictive models as modern statistical and machine learning methods that are able to assess quantitatively biological or physicochemical properties. QSAR-based virtual screening consists of direct assessment of activity values (numerical or binary) of all compounds in the database followed by selection of hits possessing desirable activity. Mathematical methods used for models preparation can be subdivided into classification and regression approaches. The former decide whether a given compound is active, whereas the latter numerically evaluate the activity values. Classification approaches that assess probability of decisions are called probabilistic. 

Auer J, Bajorath J. Emerging chemical patterns a new methodology for molecular classification and compound selection. J. Chem. 

The applications of mass spectrometry in the general field of biochemistry are numerous and only human biochemical studies and some medicinal and clinical biochemistry are discussed here. These have been further reduced by selecting a limited number of compound types and dealing with investigations within these classifications. Although some important compound classes have had to be omitted, those selected are areas where GC-MS has made, and continues to make, a meaningful contribution. 

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