Reaction databases classifications

The next question is how to represent the reacting bonds of the reaction center. We wanted to develop a method for reaction classification that can be used for knowledge extraction from reaction databases for the prediction of the products of a reaction. Thus, we could only use physicochemical values of the reactants, because these should tell us what products we obtain. 

A wider variety of reaction types involving reactions at bonds to oxygen atom bearing functional groups was investigated by the same kind of methodology [30]. Reaction classification is an essential step in knowledge extraction from reaction databases. This topic is discussed in Section 10.3.1 of this book. 

Reaction classification is an essential step in knowledge acquisition from reaction databases. 

In this section, enzymes in the EC 2.4. class are presented that catalyze valuable and interesting reactions in the field of polymer chemistry. The Enzyme Commission (EC) classification scheme organizes enzymes according to their biochemical function in living systems. Enzymes can, however, also catalyze the reverse reaction, which is very often used in biocatalytic synthesis. Therefore, newer classification systems were developed based on the three-dimensional structure and function of the enzyme, the property of the enzyme, the biotransformation the enzyme catalyzes etc. [88-93]. The Carbohydrate-Active enZYmes Database (CAZy), which is currently the best database/classification system for carbohydrate-active enzymes uses an amino-acid-sequence-based classification and would classify some of the enzymes presented in the following as hydrolases rather than transferases (e.g. branching enzyme, sucrases, and amylomaltase) [91]. Nevertheless, we present these enzymes here because they are transferases according to the EC classification. 

Once it is known which compounds need to be made, the next step is to study synthetic routes. Online literature searches may be carried out or reaction databases may be u.sed. Reaction indexing and synthesis planning are discus.sed elsewhere (see Reaction Classification Reaction Databases and Synthesis Design). There are many databases of known reactions. 

This simple classification of reaction queries illustrates the need for both comprehensive and selective reaction (and sometimes even compound) databases. There is some disagreement among chemical information specialists about whether there will still be a need for selective reaction databases in the future, in view of the expected enhancement of retrieval and post-processing procedures in large reaction databases (see Sections 4.2, 4.11, and 5). 

Both the above described applications of automatic reaction-center recognition for reaction classification (see Reaction Classification) are viable approaches to dealing with large reaction databases either create permanent subsets beforehand as databases on their own, or execute an equivalent operation as required by user demand after the. search. 

In order to create subsets of this large reaction file, which can be searched on-line via STN International or with commercial in-house retrieval systems such as REACCS/ISIS from MDL Information Systems, Inc. (USA), a sophisticated algorithm has been developed by InfoChem which identifies the different reaction types in this large database. This InfoChem Classification Algorithm is currently the only available concept for structuring large reaction databases and has, therefore, a high commercial value. 

Since many of these 2.5 million reactions of the InfoChem Reaction Database are variations of the same type of reaction, InfoChem has developed a sophisticated selection concept based on the identification of all the different individual reaction types included in this file. This so-called Classification Algorithm is also used by MDL Information Systems, San Leandro, CA. Reactions with identical reaction centers and neighboring atoms are considered to be of one reaction type. An analysis of the 2.5 million reactions led to the identification of over 390 000 different reaction types. In the ChemReact database each reaction type is represented by only one selected example reaction. ChemReact was further refined to create ChemSynth, ChemReactlOO, and ChemReact41. 

More elaborate scheme.s can he envisaged. Thus, a. self-organizing neural network as obtained by the classification of a set of chemical reactions as outlined in Section 3,5 can be interfaced with the EROS system to select the reaction that acmaliy occurs from among various reaction alternatives. In this way, knowledge extracted from rcaetion databases can be interfaced with a reaction prediction system,... 

The main software tools provided by InfoChem for the handling of databases of several million structures/reactions comprise the InfoChem Fast Search Engine (ICFSE), the InfoChem Chemistry Cartridge for Oracle (ICCARTR1DGE), the reaction center identification program (TCMAP), and the widely used reaction classification algorithm CLASSIFY . 

Step 5. Each near miss tree as such generates a set of classifications of elements which have to be put into a data-base for further statistical analysis. This means that a NMMS is not meant to generate ad-hoc reactions by management after each and every serious near miss report on the contrary, a steady build-up of such a database until statistically reliable patterns of results emerge must be allowed in order to identify structural factors in the organisation and plant instead of just unique, nonrecurring aspects. 

In the database we only need to store high-pressure-limit (i.e. thermally-equilibrated intermediates) rate estimation parameters for elementary-step reactions from this information, the molecular structure, and the thermochemical parameters (Section II.D.l) one can compute the pressure-dependent reaction rates. The elementary-step high-pressure-limit rate estimation parameters depend primarily on the local functional group structure, and so are very well-suited to functional group tree classification. 

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