IN PURSUIT OF UTOPIA: DEVELOPING ULTRA-FAST IN SILICO METHODS FOR STRUCTURAL ELUCIDATION

Nuclear magnetic resonance (NMR) is undoubtedly the most important spectroscopic technique for the structural and stereochemical elucidation of natural products. However, despite the impressive advances that have been made in this field, it is striking the large number of structures erroneously assigned based on spectroscopic data [1].

Quantum chemical calculations of NMR shifts and coupling constants emerge as a powerful and simple option to shed light on structural or stereochemical issues of complex natural products. This approach has been extensively employed in recent years to facilitate the determination of the tridimensional structure of a wide variety of organic molecules [2].

Our research team has made important contributions in this field [3]. We pioneered the use of artificial intelligence methods in the development of novel strategies of structural validation (ANN-PRA) [4]. In a conceptually different approach, we developed DP4+, a probability used to determine the most likely structure among two or more candidates when one set of experimental data is available [5]. Despite the results obtained with DP4+ are generally excellent, one of its limitations is related to the relatively large computational cost involved in obtaining DFT-optimized geometries.

In recent years, we have devoted considerable effort to developing methods that are both faster and accurate. We first introduced J-DP4, which employs J-guided conformational screening to reduce the number of candidate structures [6]. This work led to ML-J-DP4, where a machine learning scheme enables the resolution of complex structures within minutes using standard desktop resources [7]. In this lecture, I will present the results achieved along this line, with particular emphasis on our advances toward real-time accurate NMR prediction.

REFERENCES

[1] Nicolaou, K. C.; Snyder, S. A. Angew. Chem. Int. Ed. 2005, 44, 1012-1044.

[2] Grimblat, N.; Sarotti, A. M. Chem. Eur. J. 2016, 22, 12246-12261.

[3] Marcarino, M. O.; Zanardi, M. M.; Cicetti, S.; Sarotti, A. M. Acc. Chem. Res. 2020, 53, 1922-1932.

[4] Zanardi, M. M.; Sarotti, A. M. J. Org. Chem. 2015, 80, 9371-9378.

[5] Grimblat, N.; Zanardi, M. M.; Sarotti, A. M. J. Org. Chem. 2015, 80, 12526-12534.

[6] Grimblat, N.; Gavin, J. A.; Hernández Daranas, A.; Sarotti, A. M. Org. Lett. 2019, 21, 4003-4007

[7] Tsai, Y. H.; Amichetti, M.; Zanardi, M. M.; Grimson, R.; Herández Daranas, A.; Sarotti, A. M. Org. Lett. 2022, 24, 7487-7491.

Acknowledgements: CONICET, ANPCYT, UNR.