The combination of computational methods and experimental data from Nuclear Magnetic Resonance (NMR) is a considerably valuable tool in the elucidation of new natural product structures and, also, in the structural revision of previously reported compounds. Until recently, only classical statistical parameters were used, for example, linear correlation coefficient (R2 ), mean absolute error (MAE), or root mean square deviation (RMSD), as a way to statistically "validate" the structure pointed out by experimental NMR spectra. Regarding the resolution of the relative configuration of organic molecules, novel tools were available in the last few years to assist in the NMR elucidation process. The most relevant are DP4+, which is based on a Bayesian probability, and ANN-PRA, which is based on artificial neural networks. The combined application of these tools has become the most accurate and important alternative to solve structural and stereochemical problems in natural product chemistry. Therefore, herein, in this case study, we intended to promote these novel tools, exploring the strengths and limitations of each approach in resolving the relative configuration of the sesquiterpene alpha-bisabol. We also highlighted the advantages of the complementary use of H- and C-DP4+ to obtain optimal results in the differentiation of the stereoisomers, validating the proposal with ANN-PRA method.
Keywords: ANN-PRA; DP4+; NMR; epimers; molecular modeling; structural elucidation; α-bisabolol.
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