The use of CeCl3·7H2O as an efficient and eco-friendly promoter for the convenient synthesis of methoximes derived from aromatic aldehydes and ketones, is reported. The transformations entail the use of equimolar amounts of MeONH2·HCl and NaOAc in EtOH at 50°C, and no special precautions are needed with regard to the presence of oxygen. The scope and limitations of the transformation were studied and a reaction mechanism was proposed.
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 learning-inspired 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.
The isolation, synthesis and biological activity of naturally occurring furo[3,2-c] coumarins carrying carbon substituents at C-2 and/or C-3, is discussed. Synthetic approaches toward the properly functionalized heterocyclic motif are also presented.
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