A critical review on the use of DP4+ in the structural elucidation of natural products: the good, the bad and the ugly. A practical guide.

Covering: 2015 up to the end of 2020Even in the golden age of NMR, the number of natural products being incorrectly assigned is becoming larger every day. The use of quantum NMR calculations coupled with sophisticated data analysis provides ideal complementary tools to facilitate the elucidation process in challenging cases. Among the current computational methodologies to perform this task, the DP4+ probability is a popular and widely used method. This updated version of Goodman's DP4 synergistically combines NMR calculations at higher levels of theory with the Bayesian analysis of both scaled and unscaled data. Since its publication in late 2015, the use of DP4+ to solve controversial natural products has substantially grown, with several predictions being confirmed by total synthesis. To date, the structures of more than 200 natural products were determined with the aid of DP4+. However, all that glitters is not gold. Besides its intrinsic limitations, on many occasions it has been improperly used with potentially important consequences on the quality of the assignment. Herein we present a critical revision on how the scientific community has been using DP4+, exploring the strengths of the method and how to obtain optimal results from it. We also analyze the weaknesses of DP4+, and the paths to by-pass them to maximize the confidence in the structural elucidation.

NMR Calculations with Quantum Methods: Development of New Tools for Structural Elucidation and Beyond.

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 poor energy predictions provided by most DFT methods. In our latest work, we tackle this problem by averaging the results provided by randomly generated ensembles, paving the way for a new paradigm in quantum NMR-assisted structural elucidation.

Design, synthesis and evaluation of novel levoglucosenone derivatives as promising anticancer agents.

A series of levoglucosenone-derived 1,2,3-triazoles and isoxazoles featuring a flexible spacer between the heteroaromatic and anhydropyranose cores have been designed and synthesized following an hetero Michael // 1,3-dipolar cycloaddition path. The use of a design of experiments approach allowed the optimization of the oxa-Michael reaction with propargyl alcohol as nucleophile, a key step for the synthesis of the target compounds. All of the compounds were tested for their anticancer activity on MDA-MB-231 cells, featuring mutant p53. The results highlighted the importance of the introduction of the flexible spacer as well as the higher activity of oxa-Michael isoxazole-derivatives. The most prominent compounds also showed anti-proliferative activities against lung and colon cancer cell lines. The compounds showed enhanced cytotoxic effects in the presence of mutant p53, determined both by endogenous mutant p53 knock down (R280K) and by reintroducing p53 R280K in cells lacking p53 expression.