Formula structure generation

Furthermore, the prediction of and NMR spectra is of great importance in systems for automatic structure elucidation. In many such systems, aU isomers with a given molecular formula are automatically produced by a structure generator, and are then ranked according to the similarity of the spectrum predicted for each isomer to the experimental spectrum. 

The same situation obtains when the solution space is formed by candidate structures generated from any preliminary known information, e.g., an empirical formula. With any increase in the number of atoms in... 

Bangov, I.P. (1990). Computer-Assisted Structure Generation from a Gross Formula. 3. Alleviation of the Combinatorial Problem. JChem.Inf.Comput.Sci., 30,277-289. 

Bangov, l.P. (1990) Computer-assisted structure generation from a gross formula. 3. Alleviation of the combinatorial problem. /. Chem. Inf. Comput. Sci., 30, 277-289. 

Constrained Structure Generation (CONGEN) Can generate all possible isomers if the substructure elements and the molecular formula are provided... 

The number of theoretically possible chemical structures for a given molecular formula can be very large. For example, a structure generator could generate more than 151 million isomers for a molecule with the molecular formula C6H5N4O (mass =150 Da). In contrast, the Beilstein database of organic compounds contains only 273 structures for this molecular formula [4]. 

Even a small molecular formula around C q produces a large number of possible chemical structures. The introduction of one heteroatom and/or a degree of unsaturation increases the size of the problem dramatically. The molecular formulae chosen in Tab. 23.1 represent comparably small compounds, far away from typical applications in modern organic chemistry. The main problem for making structure generation programs a common routine tool is the necessity to implement aU available pieces of information from the most important spectroscopic techniques at the earliest possible step in order to avoid the combinatorial explosion and therefore... 

ASSEMBLE 2.0 is a structure generator taking molecular formula and fragments as input. On output, candidates can be ranked based on fragment spectra given on input. 

The Expert System for the Elucidation of the Structures of Organic Compounds (ESESOC) system is used by chemists for structural elucidation and presents candidate structures consistent with a molecular formula and spectroscopic data. In addition to being a structure generator, the system can extract various information, including IR, NMR and COSY as constraints. 

Molecular structure elucidation. Computer-aided structure elucidation (CASE) uses algorithms that construct all mathematically possible structural formulas for a given molecular formula and optional structural restrictions (often obtained from a spectrum). This has to be performed efficiently and without redundance (i.e. no duplicates allowed). Virtual spectra can be calculated for generated structures and compared with the experimental spectrum to rank the generated structure candidates. The corresponding algorithms that we need for such a formula-based structure generation will be described. 

We first describe formula-based generation of molecular structures. This starts with a molecular formula and takes further restrictions into account, which often allow an enormous and necessary - reduction of the search space. Then we discuss the handling of restrictions, i.e. constrained generation. 

Often the search spaces of combinatorial chemistry are described by generic structural formulas [11], and this is true in particular for patent libraries in chemistry [329]. There are only very few structure generators that allow the use of generic structural formulas to their full potential. Moreover, there is not yet a standardized and comprehensive format for the representation of generic structural formulas. 

The data structure used for molecular graphs depends on the purpose and on the problem to be solved. For example, if an efficient formula-based structure generation plays a central role, an optimal random access to the bonds is important, and so the matrix of multiplicities will be used. However, this method has rather high memory requirements. In other situations, e.g. a substructure search, fast sequential access will be favorable and only the neighborhood list is needed. A neighborhood list keeps a list of all adjacent atoms for each atom, up to three labels as well as the associated information about atoms and bonds. 

In the context of combinatorial chemistry, as well as in natural products chemistry, it is quite possible that the analyte of interest is not contained in spectrum databases. In recent years, the use of structural (or compound) databases for structure elucidation has increased due to the evolution of web-based services such as PubChem [218] and ChemSpider [259], with approximately 26 million entries each. These databases do not generally contain spectra (there are some exceptions) and as such only provide information about compounds that have been documented to exist. Although this is a smaller subset of possible structures for a given molecular formula than generating all mathematically possible structures, the same principles apply to determining the correct one as for generated structrues, without the guarantee that the correct struc-... 

In this example, we considered only cases for which the number of possible isomers is at most 10,000. Such cases are rather exceptional (see Appendix D) and even for small molecular masses there are molecular formulas with considerably more isomers. Molecular formulas with several billion isomers exist already for molecular masses of 200. Even extremely efficient structure generating algorithms are unable to generate all isomers in reasonable time for such cases, let alone store the results. Unfortunately, a molecular mass of 200 is towards the lower limit of typical analytes for MS (see Figures 8.6 and 8.7). Therefore it is extremely important to restrict a structure space prior to structure generation. Section 8.5 is dedicated to this problem. 

For the statistical considerations in the previous section we restricted ourselves to structure spaces of no more than 10,000 constitutions for a given molecular formula. In practical applications, however, such cases will be the exception rather than the rule (see Appendix D). Thus, it should be possible to determine structural properties (SP) of the analyte prior to structure generation, so that these can be used to restrict the number of generated structures. MS classifiers provide an opportunity to extract information on present or absent SP from mass spectra. 

Where fine structure is present, this information can be used to define a more exact fuzzy formula for molecular formula candidate generation, significantly limiting the number of candidates. 

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