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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]

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]

There are several CASE expert systems that have been developed, and many structures of complex natural products have been elucidated or revised using these tools. GRAPH The first publications on computer-assisted structure elucidation (CASE) appeared more than 50 years ago using IR spectra. In particular, the invention of new, more informative NMR experiments, the synergy with DFT methods, and the application of artificial intelligence and machine learning methodologies will help CASE address problems not possible presently. [Extracted from the article]

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]