Bayesian Inference Applied to NMR-Based Configurational Assignments by Floating Chirality Distance Geometry Calculations.

Using NMR data, the assignment of the correct 3D configuration and conformation to unknown natural products is of pivotal importance in pharmaceutical and medicinal chemistry. In this report, we quantify the quality and probability of structural elucidations using Bayesian inference in combination with floating chirality distance geometry simulations. Here, we will discuss the configurational analysis of three complex natural products including isopinocampheol ( 1 ), plakilactone H ( 2 ), and iodocallophycoic acid A ( 3 ) using NMR restraints of various types and in different combinations (residual dipolar couplings (RDCs) and NOE-derived distances). Our results quantitatively demonstrate how reliably molecular geometries can be inferred from experimental NMR data, unequivocally unveiling remaining assignment ambiguities. The methodology presented here can dramatically reduce the risk of incorrect structural assignments based on the overinterpretation of incomplete data in chemistry.