Further development of structure generation in the automated structure elucidation system CHEMICS

Funatsu, Kimito; Miyabayashi, Nobuyoshi; Sasaki, Shin‐ichi

doi:10.1021/ci00057a003

Cited by 83 publications

(54 citation statements)

References 6 publications

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“…All hits of the subspectra search are starting nodes SN of a graph. The overlap of a matching substructure (numbers [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] with the starting structure leads to an intermediate substructure, depicted by the nodes of the graph (letters A-S). A particular search path is terminated if the spectrum resulting from the overlap is not completely contained in the query spectrum (open circles).…”

Section: Discussionmentioning

confidence: 99%

Fully Automated Structure ElucidationA Spectroscopist's Dream Comes True

Will¹,

Fachinger²,

Richert³

1996

J. Chem. Inf. Comput. Sci.

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To meet the ever increasing demands on quality and efficiency of spectroscopic services an entirely new approach to fully automated structure elucidation has been developed. SpecSolv, as this program was titled, represents a new module for the multidimensional spectroscopic interpretation system SpecInfo. SpecSolv is a self-learning, artificially intelligent system based exclusively on 13C-NMR chemical shift, intensity and multiplicity information which is readily available from 13C-NMR-DEPT spectra. Unlike other structure generators, SpecSolv does not require any additional input from further spectroscopic techniques or even the knowledge of the molecular formula of the unknown. Using a dedicated data base of subspectra-substructure correlations (SSC) and a novel assembly algorithm, SpecSolv is capable of elucidating structures from most organic molecules (C, H, N, O, S, P, X) up to a molecular weight of about 1000 Da in only minutes of computing time.

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Section: Discussionmentioning

confidence: 99%

Fully Automated Structure ElucidationA Spectroscopist's Dream Comes True

Will¹,

Fachinger²,

Richert³

1996

J. Chem. Inf. Comput. Sci.

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show abstract

“…Tree processing of this molecule allows for the recognition of two stereocenters: double bonds at atom pairs (2,3) and (4,5), respectively, and accordingly there will be four Z/E stereoisomers with binary configurations: [0,0]; [0,1]; [1,0]; and [1,1]. Note that stereocenter atom number 2 has the same configuration as stereocenter atom number 3; the same applies for atoms 4 and 5.…”

Section: Stereoisomer Generationmentioning

confidence: 99%

“…P1:{(1,1);(2,8);(3,7);(4,6);(5,5);(9,9);(10,10)} G1:{1,5,7,3} 0 1 P2:{(1,1);(2,10);(3,5);(4,4);(6,9);(7,7);(8,8)} G2:{2,8,10,4,9,6} P3:{(1,1);(2,2);(3,3);(4,9);(5,7);(6,6);(8,10)8 have the same properties among them and different from atoms 2 and 5 that constitute another group. In…”

mentioning

confidence: 99%

Algorithm for Exhaustive and Nonredundant Organic Stereoisomer Generation

Contreras

Álvarez

Guajardo

et al. 2006

J. Chem. Inf. Model.

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Generation of organic stereoisomers with R/S, Z/E, and/or M/P configurations that may contain heteroatoms, multiple bonds, and any kind of cycle (isolated, spiro, condensed, and nested) is described. Inputs for processing are molecular structures in a N_tuple format resident on an automatic (canonical) or manual (non canonical) generated file which are processed by doing internal molecular graph construction, a weighted bipartite tree construction for all atoms and bonds to detect stereocenters, and symmetrical atom groups (SAG) with some specific SAG parameters that constitute a novel way for redundancy elimination of meso structures. Finally, determination of ligand CIP priorities allows for writing the output N_tuples with stereoisomer description. Several examples showing application of this methology to a wide number of structures are also presented.

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“…[1][2][3][4][5][6][7][8][9][10][11] Substructures, allied to other biochemical inferences, are the main tools for chemotaxonomy methodology, and may be useful to discrimimate genera, species etc. 12,13 The aim of this work is to demonstrate how a specialist system developed to assist the chemist in both fields described above can be used for chemotaxonomic purposes.…”

Section: Introductionmentioning

confidence: 99%

SISTAX: an intelligent tool for recovering information on natural products chemistry

Alvarenga

Gastmans

Ferreira

et al. 2003

J. Braz. Chem. Soc.

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Este trabalho descreve o desenvolvimento de um novo programa para o sistema especialista SISTEMAT, denominado de SISTAX. Este programa permite aos interessados em quimiotaxonomia realizar uma "pesquisa inteligente" de substâncias orgânicas em bancos de dados através de estruturas químicas. Quando acoplado com um eficiente sistema de códigos, este programa reconhece tipos de esqueletos e pode encontrar quaisquer restrições subestruturais solicitadas pelo usuário. Um exemplo da aplicação do programa para diterpenos encontrados em plantas é descrito.This work describes the development of a new program, named SISTAX, for the expert system SISTEMAT. This program allows anyone interested in chemotaxonomy to carry out an intelligent search for organic compounds in databases through chemical structures. When coupled with can efficient encoding system, the program recognizes skeletal types and can find any substructural constraints demanded by the user. An example of an application of the program to the diterpene class found in plants is described.

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Further development of structure generation in the automated structure elucidation system CHEMICS

Cited by 83 publications

References 6 publications

Fully Automated Structure ElucidationA Spectroscopist's Dream Comes True

Fully Automated Structure ElucidationA Spectroscopist's Dream Comes True

Algorithm for Exhaustive and Nonredundant Organic Stereoisomer Generation

SISTAX: an intelligent tool for recovering information on natural products chemistry

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