Towards automatically verifying chemical structures: the powerful combination of

Human interpretation of spectroscopic data remains key to confirming newly synthesised chemical structures. Whilst there have been advances in automated spectral interpretation, the false positive and false negative rates remain too high to replace human interpretation. One approach, Automated Structure Verification (ASV), scores observed nuclear magnetic resonance (NMR) spectra against predicted NMR spectra. We describe a method to extend this approach to infrared (IR) spectra and apply it alongside proton NMR spectra to distinguish between a challenging set of 99 similar isomer pairs. Based on relative scores, we classify each as correct, incorrect or unsolved. Our results show that IR can be used as an efficient automated method to distinguish similar isomers with an accuracy close to that of proton NMR. We further introduce a method to combine NMR and IR results and show that the combination significantly outperforms either technique alone. At a true positive rate of 90%, unsolved pairs are reduced to 0-15% using NMR and IR together compared to 27-49% using individual techniques alone. At a true positive rate of 95%, they are reduced to 15-30% from 39-70%. These results are a significant step towards efficient automated structure verification based on easily measured spectroscopy data.