DU8+ Computations Reveal a Common Challenge in the Structure Assignment of Natural Products Containing a Carboxylic Anhydride Moiety

Natural Occurring Terpene Cyclic Anhydrides: Biosynthetic Origin and Biological Activities

Cyclic acid anhydride is a not very widespread structure in nature, but with a determining role in natural products possessing this functionality in their skeleton. To the best of our knowledge, no revision of terpenes containing cyclic anhydrides has been previously reported. The result was that more than 100 terpenic cyclic anhydrides and related compounds were found to be in need of being reported. This review has been systematically organized by terpene skeletons, from the smallest to largest, describing their sources and bioactivities. In addition, different biosynthetic pathways for their final oxidations, namely, routes A, B and C, leading to the formation of these heterocyclic natural products, have been proposed. We have also included the most plausible precursors of these natural products, which mostly happened to be present in the same natural source. Some molecules derived from terpene cyclic anhydrides, such as their natural imide derivatives, have also been described due to their significant biological activity. In this sense, special attention has been paid to cantharidin because of its historical relevance and its broad bioactivity. A plausible biosynthesis of cantharidin has been proposed for the first time. Finally, cyclic anhydride structures that were firstly assigned as anhydrides and later corrected have been also described.

Total Synthesis and Absolute Configuration Determination of the α-Glycosidase Inhibitor (3S,4R)-6-Acetyl-3-hydroxy-2,2-dimethylchroman-4-yl (Z)-2-Methylbut-2-enoate from Ageratina grandifolia

Herein, we report the first total synthesis of α-glycosidase inhibitor (3R, 4S)-6-acetyl-3-hydroxy-2,2-dimethylchroman-4-yl (Z)-2-methylbut-2-enoate as well as its enantiomer. Our synthesis confirms the chromane structure separately proposed by Navarro-Vazquez and Mata, on the basis of DFT computations. Furthermore, our synthesis allowed us to determine the absolute configuration of the natural compound as (3S, 4R) and not (3R, 4S).

Computational Structural Revision of Elaeophorbate and Other Triterpenoids with the Help of NAPROC-13. A New Strategy for Structural Revision of Natural Products

A considerable number of natural products have been published in recent years with misassigned structure, even though they had been correctly elucidated in the past. The availability of databases containing revised structures can prevent the amplification of errors in structural elucidation. NAPROC-13, a dereplication tool based on the ¹³C chemical shift, has been used to search for substances that, possessing the same chemical shifts, have been described with different structures. The correct structure of these different structural proposals is verified by computational chemistry. This paper reports the structural revision of nine triterpenoids following this methodology.

Peculiar Reaction Products and Mechanisms Revisited with Machine Learning-Augmented Computational NMR

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.

DU8ML: Machine Learning-Augmented Density Functional Theory Nuclear Magnetic Resonance Computations for High-Throughput In Silico Solution Structure Validation and Revision of Complex Alkaloids

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.