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

Abstract

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.

A systematic approach for the generation and verification of structural hypotheses

“…Both structures are presented with the 13 C chemical shift assignment suggested by authors: [37] Steglich and Hellwig [38] have shown that structures 1 and 2 are wrong, and the following alternative structural formulae for these compounds, 3 and 4, were offered and proved: For Boletunone A, we have shown [19] that application of StrucEluc allowed reliable determination of the right structure 3 in 0.15 s. We also demonstrated how the problem of Boletunone B could be solved very quickly and correctly in a systematic way. Since the Boletunone B story was used by Bagno et al [20] for challenging DFT chemical shift calculations used as an analytical tool, we will explain here the solution of this problem in more detail.…”

“…Both algorithms are implemented in the CASE expert system Structure Elucidator, [16 -18] and their high speed and accuracy has strongly influenced the CASE strategy. [19] The third algorithm included into the set of system predictors is based on a fragment method. [1] Although this method is not as fast as the other two, it allows the user to obtain a detailed explanation as to how each predicted chemical shift was calculated.…”

“…For each atom within the candidate structure the related structures used for the prediction can be shown with their assigned chemical shifts and this allows the user to understand the origin of the predicted chemical shifts. All three methods can be used for 1 H, 13 C, 15 N, 19 F and 31 P NMR chemical shift prediction and all of them are implemented within the Structure Elucidator software program.…”

Data Processing, Metabolomic Databases and Pathway Analysis

“…Thirdly, it is very straightforward to scale up or fractionate LC-effluents to obtain larger quantities of purified metabolites for structure elucidation, for example by subsequent 2D-NMR analysis (Nakabayashi et al, 2009). Identification of NMR spectra has been greatly improved by the availability of public databases such as NMRshiftDB, MadisonMetabolome DB (Cui et al, 2008) and tools for automated elucidation of metabolites by machine-learned NMR prediction (Elyashberg et al, 2009). And last, a wider range of mass spectrometer types and ionization modes is in widespread use for LC-based separations compared to GC-options, from very sensitive QQQ machines for target quantification over ion traps and Qtraps to high-resolution mass spectrometers such as QTOFs, FT-ICR and Orbitrap instruments.…”

Atmospheric particulate matter characterization by Fourier Transform Infrared spectroscopy: a review of statistical calibration strategies for carbonaceous aerosol quantification in US measurement networks

“…For these applications, quantitative analysis has been conducted using various regression algorithms with standard gases or synthetic calibration spectra with absolute accuracies on 30 were reproduced, provided that relevant training samples were available in the library. Much of the work was motivated by pattern matching and classification of spectra for unknown samples (Robb and Munk, 1990;Novic and Zupan, 1995), and automated band assignment and identification of the underlying fragments typically performed by trained spectroscopists (Sasaki et al, 1968;Gribov and Elyashberg, 1970;Christie and Munk, 1988;Munk, 1998;Hemmer, 2007;Elyashberg et al, 2009). …”