Doubling the power of DP4 for computational structure elucidation

Area of Science:

• Computational Chemistry
• Organic Chemistry
• Spectroscopy

Background:

• Nuclear Magnetic Resonance (NMR) spectroscopy is crucial for structure elucidation.
• Computational methods, particularly density functional theory (DFT), aid NMR analysis.
• Optimizing DFT parameters is essential for accurate structure determination.

Purpose of the Study:

• To systematically screen and optimize DFT functionals and statistical models for computational NMR structure elucidation.
• To evaluate an extended PyDP4 workflow on diverse and challenging chemical structures.
• To develop tools for streamlined setup and analysis of computational and experimental NMR data.

Main Methods:

• Large-scale screening of DFT functionals and statistical models.
• Application of the extended PyDP4 workflow to 42 biologically active, stereochemically rich, and flexible compounds.
• Utilizing a MMFF/mPW1PW91/M06-2X combination with a 2 Gaussian, 1 region statistical model.

Main Results:

• The optimized workflow successfully identified the correct diastereomer among up to 32 possibilities for complex molecules.
• Achieved a 2-fold reduction in structural uncertainty.
• Realized a 7-fold reduction in model overconfidence.

Conclusions:

• The developed computational NMR structure elucidation workflow provides rapid and reliable results.
• This advancement is valuable for natural product and synthetic chemists.
• Facilitates accurate structural determination of complex organic molecules.