Abstract

While density functional theory (DFT) remains the standard for accurate simulation of nuclear magnetic resonance (NMR) spectra, its computational cost remains prohibitive. Use of DFT for structural confirmation is only justified where it offers substantial time savings over the experiment, such as total synthesis of natural products. Neural networks are a promising solution for simpler molecules, but published examples cannot estimate the prediction uncertainty.

By incorporating uncertainty estimation into an existing neural network, we can confirm the structure from its NMR spectrum 100,000 times faster than using DFT , with calculations completed in milliseconds rather than hours. Large-scale combinatorial studies show that our approach matches accuracy of DFT -based DP5 analysis and exceeds the sensitivity of simple error analysis. Analysis of 24 misassigned natural product structures demonstrates the generalisability of the method and equal performance to that of DFT .

We are now exploring the potential of the new method for automated structure revision and interpretation of 1H NMR spectra.