Breaking the DFT Energy Bias Caused by Intramolecular Hydrogen‐Bonding Interactions with MESSI, A Structural Elucidation Method Inspired by Wisdom of Crowd Theory
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 func...
Gespeichert in:
Veröffentlicht in: | Chemistry : a European journal 2023-06, Vol.29 (35), p.e202300420-n/a |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | 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.
One of the unsolved problems in NMR methods of stereochemical assignment 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. |
---|---|
ISSN: | 0947-6539 1521-3765 |
DOI: | 10.1002/chem.202300420 |