Natural Products Containing the Oxetane and Related Moieties Present Additional Challenges for Structure Elucidation: A DU8+ Computational Case Study

Abstract

Analysis of published NMR data for natural products containing the oxetane moiety, with the help of a recently developed parametric/DFT hybrid computational method DU8+, has revealed that oxetanes and related compounds constitute yet another significant challenge in structure elucidation and stereochemistry assignment, as more than 30 structures required revision. The most common pitfalls are discussed, and revised structures are suggested for 26 natural products.

Monoterpenoid Indole Alkaloids with Promoting Neurite Growth from Tabernaemontana divaricata

“…Nevertheless, the 13 C NMR computations demonstrated the potent evidence to elucidate structure. [ 12 ] Gauge‐Independent Atomic Orbital (GIAO) calculations were used for 13 C NMR chemical shifts of compounds 1 and 2 accomplished by DFT at the mPW1PW91/6‐311+g (d,p) and the mPW1PW91/6‐311+g (2d,p) level in acetone ( 1 ) and methanol ( 2 ) with the PCM solvent model, respectively. There was presence of eight possible relative epimers of 1 ( 1 a— 1 h) with consideration of chiral carbons and molecular model.…”

Misassigned Polyoxygenated Sterols and Reassignments of Their Structures

“…In this section, we describe the structural reassignment of 2 polyoxygenated sterols having 3β,7β-dihydroxy-5β,6β-epoxy and 3β,7α,11α-trihydroxy-5β,6β-epoxy structures, 5β,6βepoxy-(22E)-ergosta-8,22-diene-3β,7β-diol (4) and 5β,6βepoxyergosta-8,24(28)-diene-3β,7α,11α-triol (7).…”

“…5 On the other hand, structural misassignments of natural products are prevalent in the literature. [6][7][8] Determination of structure is a fundamental pillar of the discipline of chemistry. 9 However, misinterpretations of spectroscopic and/or physical data have often been known to result in structural misassignments.…”

A DFT/machine‐learning hybrid method for the prediction of 3JHCCH couplings

“…Also, specialized basis sets and the use of empirical calibrations [6,7] may result in important savings of computational time, as can be seen in the various contributions from Kutateladze group, [8] making J-coupling prediction a very practical tool for structure elucidation. [9] In the last few years, we have witnessed an important increase in the use of the so-called machine learning (ML) methodologies for fast prediction of molecular properties from three-dimensional structure. [10] Although the NMR field has a long tradition of different ML techniques [11] for the prediction of chemical shifts and/or scalar couplings, they are mostly devoted to prediction from a 2D graph, therefore averaging or making implicit conformational effects on the predicted NMR parameters.…”

“…Also, specialized basis sets and the use of empirical calibrations [ 6,7 ] may result in important savings of computational time, as can be seen in the various contributions from Kutateladze group, [ 8 ] making J ‐coupling prediction a very practical tool for structure elucidation. [ 9 ]…”