Computer-aided Structure Elucidation Research Articles

An efficient computer-aided structural elucidation strategy for mixtures using an iterative dynamic programming algorithm

The identification of chemical structures in natural product mixtures is an important task in drug discovery but is still a challenging problem, as structural elucidation is a time-consuming process and is limited by the available mass spectra of known natural products. Computer-aided structure elucidation (CASE) strategies seek to automatically propose a list of possible chemical structures in mixtures by utilizing chromatographic and spectroscopic methods. However, current CASE tools still cannot automatically solve structures for experienced natural product chemists. Here, we formulated the structural elucidation of natural products in a mixture as a computational problem by extending a list of scaffolds using a weighted side chain list after analyzing a collection of 243,130 natural products and designed an efficient algorithm to precisely identify the chemical structures. The complexity of such a problem is NP-complete. A dynamic programming (DP) algorithm can solve this NP-complete problem in pseudo-polynomial time after converting floating point molecular weights into integers. However, the running time of the DP algorithm degrades exponentially as the precision of the mass spectrometry experiment grows. To ideally solve in polynomial time, we proposed a novel iterative DP algorithm that can quickly recognize the chemical structures of natural products. By utilizing this algorithm to elucidate the structures of four natural products that were experimentally and structurally determined, the algorithm can search the exact solutions, and the time performance was shown to be in polynomial time for average cases. The proposed method improved the speed of the structural elucidation of natural products and helped broaden the spectrum of available compounds that could be applied as new drug candidates. A web service built for structural elucidation studies is freely accessible via the following link (http://csccp.cmdm.tw/).

On Symmetry of Some Nano Structures

It is necessary to generate the automorphism group of a chemical graph in computer-aided structure elucidation. An Euclidean graph associated with a molecule is defined by a weighted graph with adjacency matrix M = [dij], where for i≠j, dij is the Euclidean distance between the nuclei i and j. In this matrix dii can be taken as zero if all the nuclei are equivalent. Otherwise, one may introduce different weights for distinct nuclei. A.T. Balaban introduced some monster graphs and then M. Randic computed complexity indices of them (see A.T. Balaban, Rev. Roum. Chim. 18(1973) 841-853 and M. Randic, Croat. Chem. Acta 74(3)(2001) 683- 705). In this paper, we describe a simple method, by means of which it is possible to calculate the automorphism group of weighted graphs.

A Combined Atomic Force Microscopy and Computational Approach for the Structural Elucidation of Breitfussin A and B: Highly Modified Halogenated Dipeptides from Thuiaria breitfussi

Turning a new leaf: The first structures isolated from Thuiaria breitfussi, the breitfussins, are presented. This structural class consists of indole–oxazole–pyrrole units. Limited quantities prevented crystallization; therefore, the structures were solved using a novel combination of AFM, computer-aided structure elucidation (CASE), and DFT calculations. Visualization by AFM determined all the connection points of the cyclic systems and the other substituents.

Computational Strategies for Metabolite Identification in Metabolomics

Most metabolomic data are characterized by complex spectra or chromatograms containing hundreds of peaks or features. While there are many methods for aligning or comparing these spectral features, there are few approaches for actually identifying which peaks match to which compounds. Indeed, one of the biggest unmet needs in the field of metabolomics lies in the problem of compound identification. This review describes some of the newly emerging computational strategies in metabolomics that are being used to aid in the identification of metabolites from biofluid mixtures analyzed by NMR and MS. The most successful compound-identification strategies typically involve matching spectral features of the unknown compound(s) to curated spectral databases of reference compounds. This approach is known as the identification of 'known unknowns'. However, the identification of truly novel compounds (the 'unknown unknowns') is particularly challenging and requires the use of computer-aided structure elucidation methods being applied to the purified compound. The strengths and limitations of these approaches as applied to different analytical technologies (GC-MS, LC-MS and NMR) will be discussed, as will prospects for potential improvements to existing strategies.

Matching Structures to Mass Spectra Using Fragmentation Patterns: Are the Results As Good As They Look?

Three programs were assessed for their ability to predict mass spectral fragmentation patterns for all constitutional isomers of an experimental low-resolution electron impact mass spectrum (EI-MS), given the molecular formula, and use this information to identify the "correct structure". MOLGEN 3.5 was used to generate the structures, while all spectra were extracted from the NIST database. The commercial programs Mass Frontier and ACD MS Manager, as well as MOLGEN-MSF (developed by the University of Bayreuth) were used to generate mass spectral fragments. MOLGEN-MSF was used to generate "match values" to compare the different programs and their ability to identify the "correct structure". Although high match values could be achieved with certain settings, the ranking of the correct structure relative to other constitutional isomers was not significantly better than the results published previously and in some cases significantly worse. Furthermore, all programs showed bias toward specific structures, which changed significantly with minor changes to the program settings. Thus, advances in mass spectral fragment prediction have not necessarily improved computer aided structure elucidation (CASE) from EI-MS and indicate that caution must be used when confirming the identity of a compound only based on the match between its predicted fragments and the mass spectrum.

Computer-assisted methods for molecular structure elucidation: realizing a spectroscopist's dream

BackgroundThis article coincides with the 40 year anniversary of the first published works devoted to the creation of algorithms for computer-aided structure elucidation (CASE). The general principles on which CASE methods are based will be reviewed and the present state of the art in this field will be described using, as an example, the expert system Structure Elucidator.ResultsThe developers of CASE systems have been forced to overcome many obstacles hindering the development of a software application capable of drastically reducing the time and effort required to determine the structures of newly isolated organic compounds. Large complex molecules of up to 100 or more skeletal atoms with topological peculiarity can be quickly identified using the expert system Structure Elucidator based on spectral data. Logical analysis of 2D NMR data frequently allows for the detection of the presence of COSY and HMBC correlations of "nonstandard" length. Fuzzy structure generation provides a possibility to obtain the correct solution even in those cases when an unknown number of nonstandard correlations of unknown length are present in the spectra. The relative stereochemistry of big rigid molecules containing many stereocenters can be determined using the StrucEluc system and NOESY/ROESY 2D NMR data for this purpose.ConclusionThe StrucEluc system continues to be developed in order to expand the general applicability, provide improved workflows, usability of the system and increased reliability of the results. It is expected that expert systems similar to that described in this paper will receive increasing acceptance in the next decade and will ultimately be integrated directly to analytical instruments for the purpose of organic analysis. Work in this direction is in progress. In spite of the fact that many difficulties have already been overcome to deliver on the spectroscopist's dream of "fully automated structure elucidation" there is still work to do. Nevertheless, as the efficiency of expert systems is enhanced the solution of increasingly complex structural problems will be achievable.

Fuzzy Structure Generation: A New Efficient Tool for Computer‐Aided Structure Elucidation (CASE).

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Fuzzy Structure Generation: A New Efficient Tool for Computer-Aided Structure Elucidation (CASE)

Contemporary Computer-Aided Structure Elucidation (CASE) systems are heavily based on the utilization of 2D NMR spectra. The utilization of HMBC/GHMBC and COSY/GCOSY correlations generally assumes that these correlations result from (2-3)JCH and (2-3)JHH spin-spin couplings, respectively, and consequently these values are used as the default setting in these systems. Our previous studies1,2 have shown that about half of the problems studied actually contain some correlations of 4-6 bonds, so-called "nonstandard" correlations. In such cases the initial 2D NMR data are contradictory, and the correct solution is therefore not directly attainable. Unfortunately nonstandard correlations and the number of intervening bonds usually cannot be identified experimentally. In this work we suggest a new approach that we term Fuzzy Structure Generation. This allows the solution of structural problems whose 2D NMR data contain an unknown number of nonstandard correlations having different and unknown lengths. Suggested methods for the application of Fuzzy Structure Generation are described, and their application is illustrated by a series of real-world examples. We conclude that Fuzzy Structure Generation is efficient, and there is no real alternative at present in terms of a universal practical method for the structure elucidation of organic molecules from 2D NMR data.

The study on automorphism group of ESESOC

It is necessary to generate the automorphism group of a chemical graph in computer-aided structure elucidation. In this paper, an algorithm was developed by the all-paths topological symmetry algorithm to build the automorphism group of a chemical graph. A comparison of several topological symmetry algorithms reveals that the all-paths algorithm (APA) could yield the correct class of a chemical graph. It lays a foundation for the ESESOC system in computer-aided structure elucidation.

Generation of molecular graphs based on flexible utilization of the available structural information

Classical theory of chemical structure has remained the basis for a comprehensive understanding of organic chemistry. Graph theory provides an extremely useful mathematical language for the description of both chemical structure and its properties. In many practical computer applications such as structure elucidation, synthesis planning and molecular design the generation of sets of candidate-structures (molecular graphs) is required. It is essential, that the generation process is constrained by the input structural information derived from any spectral, physical, chemical, etc., data. On the other hand, in most of the cases this information is incomplete, overlapping and ambiguous, which calls for approaches reflecting these features. Several problems, such as the isomorphism problem, and the combinatorial explosion complicate additionally the generation process. They should find their solutions under these constraints. Hence, the application of any general method for graph generation does not produce satisfactory results. All this calls for the development of specific graphgeneration schemes flexibly constrained by the input structural information. Some procedures treating special classes colored graphs (describing the real chemical structure), and employing sets of subgraphs (molecular fragments), etc., are introduced within the generation scheme. Furthermore, the generation method must reflect the fuzzy character of the information usually available to the chemist and to provide a straightforward pathway to the correct solution in the process of automated reasoning. In the present paper a wide variety of problems emerging from the generation of molecular graphs in the case of computer-aided structure elucidation are addressed and their possible solutions discussed.

Computer-aided structure elucidation for arylene-bridged polysilsesquioxanes

Phenylene- and terphenylene-bridged polysilsesquioxane networks are modeled using computer-aided structure elucidation and molecular-dynamics (MD) simulations. The models are matched with analytical results such as elemental analysis, solid-state 29Si NMR, and gas-sorption porosimetry. Models which are cross-linked in every direction in three-dimensional space do not contain pore volume and are rejected. Models which are cross-linked in only two dimensions fit well with all analytical data. In conclusion, the bridged polysilsesquioxane networks seem to be formed by an aggregation of two-dimensional structures covalently or hydrogen bonded together.

Combinatorial problems in the treatment of fuzzy 13C NMR spectral information in the process of computer-aided structure elucidation: Estimation of the carbon atom hybridization and α-environment states

The information derived from various 13C nuclear magnetic resonance spectra has in most cases more fuzzy than crisp character. An approach to treat this information by means of the membership function is discussed. The combinatorial problems emerging from such a treatment are critically considered and a structure generation scheme consisting of multiple depth-first procedures is suggested. It permits to constrain the structure generation process through a non-redundant and flexible employment of the spectral information available.

Computer-aided structure elucidation methods

Computer-aided structure elucidation methodology is discussed. Major processes involved in computer-aided structure elucidation systems are partial-structure elucidation, structure generation, and structure examination. For the three representative systems CONGEN, CASE, and CHEMICS, these processes are examined. There are four necessary conditions for automated chemical structure elucidation systems: reliability, width of application, ease of modification and portability.

Computer-aided structure elucidation of organic compounds with the chemics system: Removal of Redundant Candidates by 13C-n.m.r. Prediction

A 13C-n.m.r. prediction module capable of removing inappropriate candidate structures given for an unknown compound based on the spectral data is introduced for the CHEMICS system. Given a set of candidate structures generated in the system, the routine may be used to prune off redundant candidates which have a predicted number of signals inconsistent with the observed number. It is shown that the addition of the examination module to the system makes structure elucidation by computer much more practical.