Computer-Based Structure Determination:  Then and Now

Munk, Morton E.

doi:10.1021/ci980083r

Cited by 78 publications

(96 citation statements)

References 75 publications

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“…3 b In all, six structures are found after 20 runs, which are consistent with those found by a deterministic approach. 27 P crossover ) 0.5 1 -S best /S goal (1) where S best stands for the fitness score of the best-so-far structure and S goal is the fitness score of the target structure-(s) for the unknown. For crossover operation, the value of Partition scale ranges from 4 to 8 (one-third of the skeleton atom number 23) and is determined according to…”

Section: Resultsmentioning

confidence: 99%

Evolutionary-Algorithm-Based Strategy for Computer-Assisted Structure Elucidation

Han

Steinbeck

2004

J. Chem. Inf. Comput. Sci.

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An evolutionary algorithm (EA) using a graph-based data structure to explore the molecular constitution space is presented. The EA implementation proves to be a promising alternative to deterministic approaches to the problem of computer-assisted structure elucidation (CASE). While not relying on any external database, the EA-guided CASE program SENECA is able to find correct solutions within calculation times comparable to that of other CASE expert systems. The implementation presented here significantly expands the size limit of constitutional optimization problems treatable with evolutionary algorithms by introducing novel efficient graph-based genetic operators. The new EA-based search strategy is discussed including the underlying data structures, component design, parameter optimization, and evolution process control. Typical structure elucidation examples are given to demonstrate the algorithm's performance.

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Section: Resultsmentioning

confidence: 99%

Evolutionary-Algorithm-Based Strategy for Computer-Assisted Structure Elucidation

Han

Steinbeck

2004

J. Chem. Inf. Comput. Sci.

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“…Several types of approaches have been used for chemical identification with GC×GC and MS, including: library search, pattern matching, and rule-based systems [4]. In library search, sample data are compared to reference data with associated structural information.…”

Section: Introductionmentioning

confidence: 99%

Computer language for identifying chemicals with comprehensive two-dimensional gas chromatography and mass spectrometry

Reichenbach

Kottapalli

et al. 2005

Journal of Chromatography A

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This paper describes a language for expressing criteria for chemical identification with comprehensive two-dimensional gas chromatography paired with mass spectrometry (GC×GC-MS) and presents computer-based tools implementing the language. The Computer Language for Indentifying Chemicals (CLIC) allows expressions that describe rules (or constraints) for selecting chemical peaks or data points based on multi-dimensional chromatographic properties and mass spectral characteristics. CLIC offers chromatographic functions of retention times, functions of mass spectra, numbers for quantitative and relational evaluation, and logical and arithmetic operators. The language is demonstrated with the compound-class selection rules described by Welthagen et al. [W. Welthagen, J. Schnelle-Kreis, R. Zimmermann, J. Chromatogr. A 1019 (2003) 233-249]. A software implementation of CLIC provides a calculator-like graphical user-interface (GUI) for building and applying selection expressions. From the selection calculator, expressions can be used to select chromatographic peaks that meet the criteria or create selection chromatograms that mask data points inconsistent with the criteria. Selection expressions can be combined with graphical, geometric constraints in the retention-time plane as a powerful component for chemical identification with template matching or used to speed and improve mass spectrum library searches.

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“…Several approaches have been used to automate the compound identification process in GC × GC analysis, including library search, rule-based techniques, and pattern matching [5]. In library search, sample data are compared to reference data with associated compound information in a library.…”

Section: Introductionmentioning

confidence: 99%

Peak pattern variations related to comprehensive two-dimensional gas chromatography acquisition

Reichenbach

Visvanathan

et al. 2005

Journal of Chromatography A

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Identifying compounds of interest for peaks in data generated by comprehensive two-dimensional gas chromatography (GC × GC) is a critical analytical task. Manually identifying compounds is tedious and time-consuming. An alternative is to use pattern matching. Pattern matching identifies compounds by matching previously observed patterns with known peaks to newly observed patterns with unidentified peaks. The fundamental difficulty of pattern matching comes from peak pattern distortions that are caused by differences in data acquisition conditions. This paper investigates peak pattern variations related to varying oven temperature ramp rate and inlet gas pressure and evaluates two types of affine transformations for matching peak patterns. The experimental results suggest that, over the experimental ranges, the changes in temperature ramp rate generate non-linear pattern variations and changes in gas pressure generate nearly linear pattern variations. The results indicate the affine transformations can largely remove the pattern variations and can be used for applications such as pattern matching and normalizing retention times to retention indices.

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Computer-Based Structure Determination: Then and Now

Cited by 78 publications

References 75 publications

Evolutionary-Algorithm-Based Strategy for Computer-Assisted Structure Elucidation

Evolutionary-Algorithm-Based Strategy for Computer-Assisted Structure Elucidation

Computer language for identifying chemicals with comprehensive two-dimensional gas chromatography and mass spectrometry

Peak pattern variations related to comprehensive two-dimensional gas chromatography acquisition

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