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The application of quantum‐based NMR methods for the structural elucidation of natural and unnatural products has grown significantly. However, accurately calculating the conformational landscape of flexible molecules with intricate intramolecular hydrogen bonding (IHB) networks continues to be a major challenge. In this work, we thoroughly studied the effect of entropic contributions (trough Gibbs free energies calculations) in the DP4+ performance. Our results show that to solve biased systems with strong IHB interactions requires computing the Boltzmann contributions using Gibbs free energies computed with at least triple‐ξ basis set and SMD solvation model. In response to this finding, we have updated our DP4+App, a user‐friendly Python applet that automates the entire process of calculating DP4+ probabilities. In the new version, the program allows for calculating of conformational contributions at any selected theory level, using either SCF or Gibbs free energies. [ABSTRACT FROM AUTHOR]

Diels‐Alder between nitroalkenes derived from a sugar scaffold and cyclopentadiene were examined. Depending on chemical structure of nitroalkene and polarity of solvent used, the reaction proceeded via the formation of a nitronate intermediate. Cyclic nitronates highly functionalized were obtained as chemically stable and stereochemically pure compounds. [3,3] sigmatropic shift of these ones afforded the Diels Alder cycloadducts in quantitative yield. Mechanistic aspects of reaction between nitroalkenes and cyclopentadiene has been studied using DFT computational methods. Nitronate derivatives are attractive intermediates and open the way for the construction of valuable compounds with potential applications. [ABSTRACT FROM AUTHOR]

An experimental and computational study (including DFT calculations and distortion/interaction analysis) was conducted to assess the effect of the nitrone structure in the outcome of dipolar 1,3‐cycloadditions with levoglucosenone, a biomass derived chiral enone. While B3LYP/6‐31G* (the most popular method for modeling these reactions according to our literature search) provides qualitatively good results, large outliers were found for some systems versus experimental data. An exhaustive exploration of other levels allowed us to determine the most appropriate ones to predict simultaneously reactivity and selectivity. The systematically predicted high exo selectivity by the majority of the levels led us to reconsider our initial assignment for the reaction with the nitrone derived from piperidine, which resulted in the discovery of an interesting case of conformational dynamics. [ABSTRACT FROM AUTHOR]

Structure elucidation is a stage of paramount importance in the discovery of novel compounds because molecular structure determines their physical, chemical and biological properties. Computational prediction of spectroscopic data, mainly NMR, has become a widely used tool to help in such tasks due to its increasing easiness and reliability. However, despite the continuous increment in CPU calculation power, classical quantum mechanics simulations still require a lot of effort. Accordingly, simulations of large or conformationally complex molecules are impractical. In this context, a growing number of research groups have explored the capabilities of machine learning (ML) algorithms in computational NMR prediction. In parallel, important advances have been made in the development of machine learninginspired methods to correlate the experimental and calculated NMR data to facilitate the structural elucidation process. Here, we have selected some essential papers to review this research area and propose conclusions and future perspectives for the field. [ABSTRACT FROM AUTHOR]

The use of quantum‐based NMR methods to complement and guide the connectivity and stereochemical assignment of natural and unnatural products has grown enormously. One of the unsolved problems is related to the improper calculation of the conformational landscape of flexible molecules that have functional groups capable of generating a complex network of intramolecular H‐bonding (IHB) interactions. Here the authors present MESSI (Multi‐Ensemble Strategy for Structural Identification), a method inspired by the wisdom of the crowd theory that breaks with the traditional mono‐ensemble approach. By including independent mappings of selected artificially manipulated ensembles, MESSI greatly improves the sense of the assignment by neutralizing potential energy biases. [ABSTRACT FROM AUTHOR]

This article, published in the European Journal of Organic Chemistry, discusses the synthesis of cyclic nitronates using nitroalkenes derived from carbohydrates and cyclopentadiene. The authors conducted an experimental study and used DFT calculations to support their findings. The resulting cyclic nitronates were enantiomerically pure and chemically stable. For more information, readers can refer to the research article by M.I. Mangione and co-workers. [Extracted from the article]