The DP4 probability is one of the most sophisticated and popular approaches for the stereochemical assignment of organic molecules using GIAO NMR chemical shift calculations when only one set of experimental data is available. In order to improve the performance of the method, we have developed a modified probability (DP4+), whose main differences from the original DP4 are the inclusion of unscaled data and the use of higher levels of theory for the NMR calculation procedure. With these modifications, a significant improvement in the overall performance was achieved, providing accurate and confident results in establishing the stereochemistry of 48 challenging isomeric compounds.
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.
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.
DP4+
is a popular tool for structural elucidation using GIAO NMR
calculations. The method was built with 16 statistical parameters
[μ,σ,ν], which depend on the level of theory. Herein,
we deeply analyzed the sensitivity of DP4+ when using improper [μ,σ,ν]
sets, a common situation found in the literature. The results led
us to develop a customizable DP4+ methodology that allows preliminary
calculations at any desired level of theory using a small set of training
molecules.
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