This review provides a critical revision of DP4+, a popular computational method for structural elucidation using DFT NMR calculations. Their strengths and weaknesses are explored, including paths to obtain optimal results.

Conspectus Structural elucidation is an important and challenging stage in the discovery of new organic molecules. Single-crystal X-ray analysis provides the most unquestionable results, though in practice the availability of suitable crystals limits its broad use. On the other hand, NMR spectroscopy has become the leading and universal technique to accomplish the task. Despite continuous advances in the field, the misinterpretation of NMR data is commonplace, evidenced by the large number of erroneous structures being published in top journals. Quantum calculations of NMR chemical shifts and scalar coupling constants emerged as ideal complements to facilitate the elucidation process when experimental NMR data is inconclusive. Since seminal reports demonstrated that affordable DFT methods provide NMR predictions accurate enough to differentiate among closely related isomers, the discipline has experienced substantial growth. The impact has been felt in different areas, and nowadays the results of such calculations are routinely seen in high impact literature. This Account describes our investigations in the field of quantum NMR calculations, focusing on the development of tools for structural elucidation and practical applications. We pioneered the use of artificial intelligence methods in the development of novel strategies of structural validation. Our first generation of trained artificial neural networks (ANNs) showed excellent ability to identify mistakes at the atom connectivity level, whereas the use of multidimensional pattern recognition pushed the performance to the stereochemical limit. In a conceptually different approach, we developed DP4+, an updated version of the DP4 probability used to determine the most likely structure among two or more candidates when one set of experimental data is available. Increasing the level of theory in NMR calculations and including unscaled data in the formalism improved the performance of the method, further validated to settle the configuration of challenging motifs such as spiroepoxides or Mosher’s derivatives. One of the limitations of DP4+ is related to the relatively large computational cost involved in obtaining DFT-optimized geometries, which led to the development of a fast variant including the valuable information provided by coupling constants (J-DP4 method). These tools were explored to suggest the most probable structure of controversial natural or unnatural products originally misassigned, with some predictions further validated by synthesis (as in the case of pseudorubriflordilactone B). The possibility of predicting the structure of a natural product without requiring authentic sample was investigated in collaboration with Prof. Pilli (UNICAMP, Brazil) in the computer-guided total synthesis and stereochemical revisions of several natural products. Despite these advances, there remain considerable challenges, such as the case of configurational assessment of polar systems featuring multiple intramolecular hydrogen bonding interactions because of the po...

The in silico assignment of polyhydroxylated compounds represents a major challenge given the thus far unsolved problem of inappropriate description of the conformational amplitudes. Herein, we report a conceptually novel stochastic approach based on the creation and evaluation of random artificial ensembles, which could provide a new paradigm for computing NMR properties of flexible molecules. The strategy was successfully tested under the DP4/DP4+ platforms using a large set of compounds belonging to the hyacinthacine family.

Managing and processing hundreds or thousands of files to perform an in silico structural assignment can be tedious work. Recent years have witnessed a booming need for automation to facilitate the task. In this work, we explore the repercussions of the energy mismatch made by a popular script to streamline the process. The results led us to question the risks of automation and the sensitivity toward energy miscalculations.

The discovery of efficient organocatalysts is generally carried out by thorough experimental screening of different candidates. We recently reported an efficient organocatalyst for iminium-ion-based asymmetric Diels–Alder reactions following a rational design approach. This result encouraged us to test this optimal catalyst in the mechanistically related Friedel–Crafts alkylation of indoles, but to our surprise, almost null enantioselectivity was observed. The results did not significantly improve with structurally related catalysts, and a totally unexpected facial selectivity inversion was also noticed. Using DFT calculations by modeling the competing transition structures with ONIOM, we could unravel the origins of those findings, further employed to predict the most efficient catalyst for this new transformation. The computational results were validated experimentally (up to 92:8 er), providing another successful example of a general strategy to accelerate catalyst development which still remains underexplored.

A stereoselective multicomponent reaction involving Meldrum's acid, a conjugated dienal, and an alcohol is reported. Valuable cyclopenta[ b]furan-2-ones are obtained as products of this straightforward transformation, which is accompanied by the formation of four stereocenters, two new cycles, and four new bonds (two C-C and two C-heteroatom). A reaction mechanism was elaborated involving an initial Knoevenagel condensation followed by cycloisomerization and eventual fragmentation.

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.

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 we 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.