Doubling the power of DP4 for computational structure elucidation

Abstract

A large-scale optimisation of density functional theory (DFT) conditions for computational NMR structure elucidation has been conducted by systematically screening the DFT functionals and statistical models. The extended PyDP4 workflow was tested on a diverse and challenging set of 42 biologically active and stereochemically rich compounds, including highly flexible molecules. MMFF/mPW1PW91/M06-2X in combination with a 2 Gaussian, 1 region statistical model was capable of identifying the correct diastereomer among up to an upper limit of 32 potential diastereomers. Overall a 2-fold reduction in structural uncertainty and a 7-fold reduction in model overconfidence have been achieved. Tools for rapid set-up and analysis of computational and experimental results, as well as for the statistical model generation, have been developed and are provided. All of this should facilitate rapid and reliable computational NMR structure elucidation, which has become a valuable tool to natural product chemists and synthetic chemists alike.