The aim of this study was to demonstrate the absolute necessity of control experiments for a correct interpretation of mercury drop test results when applied to mechanistic studies of palladacycle-catalyzed reactions. It was shown that the interaction of diverse azapalladacycles with metallic mercury leads to the formation of organomercuric chlorides during the redox-transmetalation process. The structure of these organomercurials was confirmed by elemental analysis, 1H, 13C­{1H}, and 199Hg­{1H} NMR spectra, X-ray diffraction analysis, and DFT calculations. The behavior and properties of C,N-mercuracycles bearing the weak and labile N···Hg bond are discussed on the basis of the temperature dependence of the NMR spectra and calculated thermodynamic parameters of the dechelation process.

Machine learning (ML) profoundly improves the accuracy of the fast DU8+ hybrid density functional theory/parametric computations of nuclear magnetic resonance spectra, allowing for high throughput in silico validation and revision of complex alkaloids and other natural products. Of nearly 170 alkaloids surveyed, 35 structures are revised with the next-generation ML-augmented DU8 method, termed DU8ML.

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DU8ML, a fast and accurate machine learning-augmented density functional theory (DFT) method for computing nuclear magnetic resonance (NMR) spectra, proved effective for high-throughput revision of misassigned natural products. In this paper, we disclose another important aspect of its application: correction of unusual reaction mechanisms originally proposed because of incorrect product structures.

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.