Abstract
Accurate calculation of specific spectral properties for NMR is an important step for molecular structure elucidation. Here we report the development of a novel machine learning technique for accurately predicting chemical shifts of both {^1mathrm{H}} and {^{13}mathrm{C}} nuclei which exceeds DFT-accessible accuracy for {^{13}mathrm{C}} and {^1mathrm{H}} for a subset of nuclei, while being orders of magnitude more performant. Our method produces estimates of uncertainty, allowing for robust and confident predictions, and suggests future avenues for improved performance.
Highlights
Nuclear magnetic resonance (NMR) spectroscopy is an established method in analytical chemistry
We identified all molecules in nmrshiftdb2 with annotated 13C or 1H chemical shift values containing only the elements {H, C, O, N, P, S, F, Cl}, excluding elements with a very low occurrence in nmrshiftdb2
To compare with DFT methods we identified a subset of 177 molecules in nmrshiftdb which had the greatest number of independent spectral measurements, and followed best practices for ab initio calculation chemical shift values
Summary
Nuclear magnetic resonance (NMR) spectroscopy is an established method in analytical chemistry. In contrast to other spectroscopic techniques like mass spectrometry (MS), it is non-destructive; in contrast to various optical spectroscopic techniques, it can often give sufficient information to completely elucidate the structure of an unknown molecule. NMR is an essential tool in many fields of chemistry and biology. In NMR one major source of information is the specific resonance frequency, termed the chemical shift, at a given spin-active nucleus in a molecule (here we focus on 1H and 13C ). The local molecular environment around a nucleus determines its chemical shift, leading to various “rules of thumb” that are taught to undergraduate organic chemists. The development of “pure-shift” NMR pulse sequences [1], which can accurately measure chemical shift values with neither homo- nor heteronuclear coupling, makes it even easier to identify precise chemical shift values in crowded spectra
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