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Using computational methods and chemical intuition, the proposed structure of janthinolide A is shown to be incorrect. It is further shown that the material described as janthinolide A is highly likely to be janthinolide C. [ABSTRACT FROM AUTHOR]

Natural products remain one of the major sources of coveted, biologically active compounds. Each isolated compound undergoes biological testing, and its structure is usually established using a set of spectroscopic techniques (NMR, MS, UV-IR, ECD, VCD, etc.). However, the number of erroneously determined structures remains noticeable. Structure revisions are very costly, as they usually require extensive use of spectroscopic data, computational chemistry, and total synthesis. The cost is particularly high when a biologically active compound is resynthesized and the product is inactive because its structure is wrong and remains unknown. In this paper, we propose using Computer-Assisted Structure Elucidation (CASE) and Density Functional Theory (DFT) methods as tools for preventive verification of the originally proposed structure, and elucidation of the correct structure if the original structure is deemed to be incorrect. We examined twelve real cases in which structure revisions of natural products were performed using total synthesis, and we showed that in each of these cases, time-consuming total synthesis could have been avoided if CASE and DFT had been applied. In all described cases, the correct structures were established within minutes of using the originally published NMR and MS data, which were sometimes incomplete or had typos. [ABSTRACT FROM AUTHOR]

The UHPLC–HRMS analysis of Cortinarius ominosus basidiomata extract revealed that this mushroom accumulated elevated yields of an unreported specialized metabolite. The molecular formula of this unknown compound, C17H10O8, indicated that a challenging structure elucidation lay ahead, owing to its critically low H/C atom ratio. The structure of this new isolate, namely ominoxanthone (1), could not be solved from the interpretation of the usual set of 1D/2D NMR data that conveyed too limited information to afford a single, unambiguous structure. To remedy this, a Computer-Assisted Structure Elucidation (CASE) workflow was used to rank the different possible structure candidates consistent with our scarce spectroscopic data. DFT-based chemical shift calculations on a limited set of top-ranked structures further ascertained the determined structure for ominoxanthone. Although the determined scaffold of ominoxanthone is unprecedented as a natural product, a plausible biosynthetic scenario involving a precursor known from cortinariaceous sources and classical biogenetic reactions could be proposed. [ABSTRACT FROM AUTHOR]

Natural products continue to be reported at an astonishing rate from a wide range of multidisciplinary research activities in the pursuit of understanding the chemistry of biodiversity. However, the elucidation of chemical structure in the modern era is heavily reliant on the analysis and interpretation of multiple spectroscopic outputs, and in most cases this activity is by no means trivial. Structural errors continue to be described given the inherent complexity of natural products. Computer‐Assisted Structure Elucidation (CASE) continues to provide improved resolving power in this regard, but for enhanced accuracy quantum chemical spectrum prediction methodology is paramount. Reported herein are a range of counterfactual natural products, identified through chemical principal screening, which have been reassigned using a combination of chemical intuition, chemical synthesis, CASE and DU8+ spectrum prediction. [ABSTRACT FROM AUTHOR]

Unusual polyenols that defied chemical principles were reassigned as the nucleosides, adenosine and uridine, using a combination of chemical intuition underpinned by Computer Assisted Structure Elucidation (CASE) and DFT methods. [ABSTRACT FROM AUTHOR]

The first efforts for the development of methods for Computer‐Assisted Structure Elucidation (CASE) were published more than 50 years ago. CASE expert systems based on one‐dimensional (1D) and two‐dimensional (2D) Nuclear Magnetic Resonance (NMR) data have matured considerably by now. The structures of a great number of complex natural products have been elucidated and/or revised using such programs. In this article, we discuss the most likely directions in which CASE will evolve. We act on the premise that a synergistic interaction exists between CASE, new NMR experiments, and methods of computational chemistry, which are continuously being improved. The new developments in NMR experiments (long‐range correlation experiments, pure‐shift methods, coupling constants measurement and prediction, residual dipolar couplings [RDCs]), and residual chemical shift anisotropies [RCSAs], evolution of density functional theory (DFT), and machine learning algorithms will have an influence on CASE systems and vice versa. This is true also for new techniques for chemical analysis (Atomic Force Microscopy [AFM], "crystalline sponge" X‐ray analysis, and micro‐Electron Diffraction [micro‐ED]), which will be used in combination with expert systems. We foresee that CASE will be utilized widely and become a routine tool for NMR spectroscopists and analysts in academic and industrial laboratories. We believe that the "golden age" of CASE is still in the future. [ABSTRACT FROM AUTHOR]

Computer‐assisted structure elucidation (CASE) is the class of expert systems that derives molecular structures primarily from one‐dimensional and two‐dimensional nuclear magnetic resonance data. Contemporary CASE systems, including Advanced Chemistry Development/Structure Elucidator (ACD/SE), consider cross‐peaks in heteronuclear multiple bond coherence (HMBC) and correlation spectroscopy (COSY) spectra as two‐ or three‐bond correlations by default. However, four and more bond correlations (nonstandard correlations [NSCs]) could be present in these spectra too. The indiscriminate addition of NSCs to the CASE computations is prohibitively expensive. To address this problem, the ACD/SE program performs a logical analysis of observed correlations and determines the minimum number of NSCs. Guided by this information, a more efficient fuzzy structure generation (FSG) algorithm is subsequently applied. Until now, the FSG algorithm was utilized without any verification of the reliability of found NSCs. Here, we report a verification method for NSCs based on the relationship between NSCs and J‐couplings computed with high accuracy density functional theory (DFT) methods. We used the example of strychnine to show that 41 (32%) of 8‐Hz HMBC cross‐peaks were NSCs and were consistent with 4–6JCH couplings greater than 0.3 Hz. This cutoff value was largely confirmed by the analysis of NSCs in 11 real‐world natural products elucidated by ACD/SE. Additionally, utilizing the example of the CASE study of cleospinol A, we showed that the DFT‐computed J‐couplings of NSCs can distinctively differentiate the correct structure among six proposed isomers. The proposed approach of NSC verification should further improve the robustness of CASE analysis and can help reveal potential problems with reported experimental data. [ABSTRACT FROM AUTHOR]

Even though NMR has found countless applications in the field of small molecule characterization, there is no standard file format available for the NMR data relevant to structure characterization of small molecules. A new format is therefore introduced to associate the NMR parameters extracted from 1D and 2D spectra of organic compounds to the proposed chemical structure. These NMR parameters, which we shall call NMReDATA (for nuclear magnetic resonance extracted data), include chemical shift values, signal integrals, intensities, multiplicities, scalar coupling constants, lists of 2D correlations, relaxation times, and diffusion rates. The file format is an extension of the existing Structure Data Format, which is compatible with the commonly used MOL format. The association of an NMReDATA file with the raw and spectral data from which it originates constitutes an NMR record. This format is easily readable by humans and computers and provides a simple and efficient way for disseminating results of structural chemistry investigations, allowing automatic verification of published results, and for assisting the constitution of highly needed open‐source structural databases. [ABSTRACT FROM AUTHOR]

Computer-assisted structure elucidation (CASE) is composed of two steps: (a) generation of all possible structural isomers for a given molecular formula and 2D NMR data (COSY, HSQC, and HMBC) and (b) selection of the correct isomer based on empirical chemical shift predictions. This method has been very successful in solving structural problems of small organic molecules and natural products. However, CASE applications are generally limited to structural isomer problems and can sometimes be inconclusive due to insufficient accuracy of empirical shift predictions. Here, we report a synergistic combination of a CASE algorithm and density functional theory calculations that broadens the range of amenable structural problems to encompass protondeficient molecules, molecules with heavy elements (e.g., halogens), conformationally flexible molecules, and configurational isomers. [ABSTRACT FROM AUTHOR]

The state of the art and recent developments in application of nuclear magnetic resonance (NMR) for structure elucidation and identification of small organic molecules are discussed. The recently suggested new two-dimensional (2D)-NMR experiments combined with the advanced instrumentation allow structure elucidation of new organic compounds at a sample amount of less than 10 µg. A pure shift approach that provides 1 H-decoupled proton spectra drastically simplified 1 H and 2D NMR spectra interpretation. The structure elucidation of extremely hydrogen-deficient compounds was dramatically facilitated due to the methodology based on combination of new 2D-NMR experiments providing long-range heteronuclear correlations with computer-assisted structure elucidation (CASE). The capabilities of CASE systems are discussed. The role of NMR-spectrum prediction in structure verification and NMR approaches for qualitative mixture analysis are considered. [ABSTRACT FROM AUTHOR]

The first methods associated with the Computer-Assisted Structure Elucidation (CASE) of small molecules were published over fifty years ago when spectroscopy and computer science were both in their infancy. The incredible leaps in both areas of technology could not have been envisaged at that time, but both have enabled CASE expert systems to achieve performance levels that in their present state can outperform many scientists in terms of speed to solution. The computer-assisted analysis of enormous matrices of data exemplified 1D and 2D high-resolution NMR spectroscopy datasets can easily solve what just a few years ago would have been deemed to be complex structures. While not a panacea, the application of such tools can provide support to even the most skilled spectroscopist. By this point the structures of a great number of molecular skeletons, including hundreds of complex natural products, have been elucidated using such programs. At this juncture, the expert system ACD/Structure Elucidator is likely the most advanced CASE system available and, being a commercial software product, is installed and used in many organizations. This article will provide an overview of the research and development required to pursue the lofty goals set almost two decades ago to facilitate highly automated approaches to solving complex structures from analytical spectroscopy data, using NMR as the primary data-type. [ABSTRACT FROM AUTHOR]

The article focuses on the use of computer-assisted structure elucidation (CASE) to avoin incorrect structure and subsequent analytical work. Topics include the structure of sinensilactam A, a meroterpenoidal hybrid metabolite, the created molecular connectivity diagram (MCD), and the use of fuzzy structure generation.

Abstract: In this article we describe a fast and accurate method of 13C NMR chemical shift prediction. The high speed of chemical shift calculation described is achieved using a simple structure description scheme based on individual atoms rather than functional groups. The systematic choice of an appropriate encoding scheme and the usage of partial least squares regression on a large training set has resulted in a robust and fast algorithm. The approach provides accuracy comparable with other well known approaches but demonstrates accelerated calculation speeds of up to a thousand times faster. [Copyright &y& Elsevier]

Biodiscovery efforts in Indonesia have aimed to explore the understudied chemical diversity of its rich lichen flora, seeking to find new products endowed with significant biological properties. The chemical screening of a Teloschistes flavicans extract led to selection of this species for further investigation. LC/MS and 1 H NMR-based dereplication pinpointed six chlorodepsidones from the thallus of a sample of this lichen. This led to the streamlined isolation and the subsequent structure elucidation of the three new compounds norflavicansone 1 , flavicansone 2 , and isocaloploicin 3 , along with the known chlorodepsidones 4 - 6 , stictic acid 7 , aurantiamide acetate 8 , and parietin 9 . The challenging structure elucidation of these proton-deficient metabolites benefited from a state-of-the-art workflow involving a synergistic combination of Computer-Assisted Structure Elucidation (CASE) and Density Functional Theory (DFT) calculations of the top-ranked candidates. This investigation also led to the revision of flavicansone's structure, previously described from this species. The three new molecules that are being reported here are remarkable in that they represent hybrid depsidones in which one of the aromatic rings is derived from orsellinic acid and the other is derived from β-orcinol, a rare structural feature for lichen depsidones.

Structure elucidation of complex natural products and new organic compounds remains a challenging problem. To support this endeavor, CASE (computer-assisted structure elucidation) expert systems were developed. These systems are capable of generating a set of all possible structures consistent with an ensemble of 2D NMR data followed by selection of the most probable structure on the basis of empirical NMR chemical shift prediction. However, in some cases, empirical chemical shift prediction is incapable of distinguishing the correct structure. Herein, we demonstrate for the first time that the combination of CASE and density functional theory (DFT) methods for NMR chemical shift prediction allows the determination of the correct structure even in difficult situations. An expert system, ACD/Structure Elucidator, was used for the CASE analysis. This approach has been tested on three challenging natural products: aquatolide, coniothyrione, and chiral epoxyroussoenone. This work has demonstrated that the proposed synergistic approach is an unbiased, reliable, and very efficient structure verification and de novo structure elucidation method that can be applied to difficult structural problems when other experimental methods would be difficult or impossible to use.

The elucidated structure of asperjinone (1), a natural product isolated from thermophilic Aspergillus terreus, was revised using the expert system Structure Elucidator. The reliability of the revised structure (2) was confirmed using 180 structures containing the (3,3-dimethyloxiran-2-yl)methyl fragment (3) as a basis for comparison and whose chemical shifts contradict the suggested structure (1).

The accuracy of (13)C chemical shift prediction by both DFT GIAO quantum-mechanical (QM) and empirical methods was compared using 205 structures for which experimental and QM-calculated chemical shifts were published in the literature. For these structures, (13)C chemical shifts were calculated using HOSE code and neural network (NN) algorithms developed within our laboratory. In total, 2531 chemical shifts were analyzed and statistically processed. It has been shown that, in general, QM methods are capable of providing similar but inferior accuracy to the empirical approaches, but quite frequently they give larger mean average error values. For the structural set examined in this work, the following mean absolute errors (MAEs) were found: MAE(HOSE) = 1.58 ppm, MAE(NN) = 1.91 ppm and MAE(QM) = 3.29 ppm. A strategy of combined application of both the empirical and DFT GIAO approaches is suggested. The strategy could provide a synergistic effect if the advantages intrinsic to each method are exploited., (Copyright (c) 2010 John Wiley & Sons, Ltd.)

During the process of molecular structure elucidation the selection of the most probable structural hypothesis may be based on chemical shift prediction. The prediction is carried out using either empirical or quantum-mechanical (QM) methods. When QM methods are used, NMR prediction commonly utilizes the GIAO option of the DFT approximation. In this approach the structural hypotheses are expected to be investigated by scientist. In this article we hope to show that the most rational manner by which to create structural hypotheses is actually by the application of an expert system capable of deducing all potential structures consistent with the experimental spectral data and specifically using 2D NMR data. When an expert system is used the best structure(s) can be distinguished using chemical shift prediction, which is best performed either by an incremental or neural net algorithm. The time-consuming QM calculations can then be applied, if necessary, to one or more of the 'best' structures to confirm the suggested solution., (Copyright (c) 2009 John Wiley & Sons, Ltd.)

The reliable determination of stereocenters contained within chemical structures usually requires utilization of NMR data, chemical derivatization, molecular modeling, quantum-mechanical (QM) calculations and, if available, X-ray analysis. In this article, we show that the number of stereoisomers which need to be thoroughly verified, can be significantly reduced by the application of NMR chemical shift calculation to the full stereoisomer set of possibilities using a fragmental approach based on HOSE codes. The applicability of this suggested method is illustrated using experimental data published for a series of complex chemical structures., (Copyright (c) 2009 John Wiley & Sons, Ltd.)

The efficacy of neural network (NN) and partial least-squares (PLS) methods is compared for the prediction of NMR chemical shifts for both 1H and 13C nuclei using very large databases containing millions of chemical shifts. The chemical structure description scheme used in this work is based on individual atoms rather than functional groups. The performances of each of the methods were optimized in a systematic manner described in this work. Both of the methods, least-squares and neural network analyses, produce results of a very similar quality, but the least-squares algorithm is approximately 2--3 times faster.

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.

Expert systems for spectroscopic molecular structure elucidation have been developed since the mid-1960s. Algorithms associated with the structure generation process within these systems are deterministic; that is, they are based on graph theory and combinatorial analysis. A series of expert systems utilizing 2D NMR spectra have been described in the literature and are capable of determining the molecular structures of large organic molecules including complex natural products. Recently, an opinion was expressed in the literature that these systems would fail when elucidating structures containing more than 30 heavy atoms. A suggestion was put forward that stochastic algorithms for structure generation would be necessary to overcome this shortcoming. In this article, we describe a comprehensive investigation of the capabilities of the deterministic expert system Structure Elucidator. The results of performing the structure elucidation of 250 complex natural products with this program were studied and generalized. The conclusion is that 2D NMR deterministic expert systems are certainly capable of elucidating large structures (up to about 100 heavy atoms) and can deal with the complexities associated with both poor and contradictory spectral data.