Structural characterisation of natural products by means of quantum chemical calculations of NMR parameters: new insights

• Published in: Organic chemistry frontiers an international journal of organic chemistry Vol. 8; no. 9; pp. 2019 - 2058
• Main Authors: Costa, Fabio L P, Ana C F de Albuquerque, Fiorot, Rodolfo G, Lião, Luciano M, Martorano, Lucas H, Mota, Gunar V S, Valverde, Alessandra L, Carneiro, José W M, Fernando M dos Santos Junior
• Format: Journal Article
• Language: English
• Published: London Royal Society of Chemistry 07.05.2021
• Subjects: Computer applications; Coupling (molecular); Data processing; Learning algorithms; Literature reviews; Machine learning; Natural products; NMR; Nuclear magnetic resonance; Organic chemistry; Parameters; Quantum chemistry
• DOI: 10.1039/d1qo00034a
• ISSN: 2052-4110

In the early 2000s, the first articles regarding the calculation of NMR parameters for natural products appeared in the literature. Since then, modelling 1H and 13C chemical shifts and spin–spin coupling constants for this class of compounds has experienced a remarkable increase in precision, accessibility, and application, leading to considerable advances in the field. More recently, significant contributions from several authors have led to continuous growth in this research field, updating and broadening the simulation of NMR parameters, in particular with the application of new techniques for data treatment. Nowadays, such studies are routinely found in the high impact literature. In this review, we intend to cover the general guidelines and the main advances in NMR calculations of natural products published since 2012. We intend to address the bottlenecks of quantum chemical calculations of NMR parameters, including mathematical definitions, updates, and a discussion of relevant examples, and to highlight novel tools, for example DU8+, CP3, DP4, DP4+ and J-DP4. We will cover all aspects of NMR simulation focusing on natural products, from the fundamentals to the new computational toolboxes available, combining advanced quantum chemical calculations with complex upstream data processing and machine learning.