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Combining infrared (IR) spectra with proton nuclear magnetic resonance (NMR) improved how well an automated method ranked proposed chemical structures in a 2025 benchmark of closely related isomers. The method verifies candidates already on the table; it does not infer any molecule from spectra alone. In that test, using evidence from both techniques reduced the share of comparisons left unresolved while maintaining selected true-positive rates.
What automated structure verification does
Automated structure verification (ASV) tests whether a proposed molecular structure fits experimental data. It ranks candidate structures against measured spectra, which is different from unconstrained structure elucidation: generating a complete structure when no candidates have been supplied.
In the study, Rowlands and colleagues combined an IR comparison algorithm called IR.Cai with proton NMR scoring. The approach was evaluated on 42 drug-like compounds through 99 comparisons between correct structures and closely related, incorrect isomers. Those results describe this constructed benchmark, not an accuracy rate for arbitrary unknown molecules or routine laboratory samples.
Why use IR and NMR together?
IR adds evidence from bond vibrations
Infrared spectroscopy measures absorption associated with molecular bond vibrations. Its spectral features, including information in the fingerprint region, can help distinguish structures that are similar but not identical.
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Proton NMR adds atom-focused evidence
Proton NMR chemical shifts provide a different view of a candidate structure. The study used DP4*, a modified NMR scoring method that excludes outlying shifts associated with exchangeable protons, which can be difficult to predict.
Because the two techniques provide complementary evidence, their combination can help where either one alone leaves a close-isomer comparison ambiguous. The paper reports that combining scores across IR and NMR improved the comparison metric; combining two scores from the same modality did not show the same improvement in the reported controls.
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How the fusion method ranks candidates
IR.Cai compares an experimental IR spectrum with calculated spectra using spectrum overlap. The combined method does not treat IR and NMR raw scores as if they were directly interchangeable. Instead, it takes each candidate’s percentile rank in the IR and NMR score lists and averages those ranks. A candidate must therefore perform well across both modalities to achieve a strong combined rank.
For its high-level IR calculations, the study used the 1250–1600 cm⁻¹ range. The authors chose that range because DMSO-d6 absorbs strongly near 1100 cm⁻¹ and extending the range higher did not improve results on this dataset. It is a study-specific choice, not a universal rule for IR analysis.
What the benchmark results show
The method reports comparisons as correct, incorrect, or unsolved. A score-difference threshold determines how confidently it makes a decision; raising the required confidence can leave more pairs unresolved. The figures below reflect that trade-off on the study’s candidate comparisons.
| Selected true-positive rate | IR and NMR together: unresolved pairs | Individual techniques: unresolved pairs |
|---|---|---|
| 90% | 0–15% | 27–49% |
| 95% | 15–30% | 39–70% |
The individual-technique ranges cover the methods reported in the paper; the proportion left unresolved varies by technique and scoring approach. At a 90% true-positive rate, the authors also report that approximately 73% of pairs were solved using high-level IR calculations alone. Adding IR to DP4* NMR scores raised that figure to 85%; combining IR with ACD NMR scores raised it to 100%.
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Across the challenging dataset, the authors state that combined IR and NMR could solve all potential comparisons at an 85% true-positive rate, with a classification-area (CA) score of 0.966. These are benchmark metrics, not a promise that the method will always decide correctly. The paper notes that relative performance between modalities depends on the test set.
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- Established: In this benchmark of close-isomer candidate comparisons, combining IR and NMR improved ranking and reduced unresolved pairs at the reported true-positive-rate thresholds.
- Not established: That the method can discover an arbitrary molecule from spectra without candidate structures, or that the same figures apply to other molecules, instruments, laboratories, or datasets.
- Human review remains important: The authors write in the abstract that “Whilst there have been advances in automated spectral interpretation, the false positive and false negative rates remain too high to replace human interpretation.”
- Calculations remain a requirement: The study says density functional theory (DFT) calculations are currently needed to simulate the NMR and IR spectra used by the approach.
Paper and data access
The full paper is available through PubMed Central. The authors state that recorded IR and NMR spectra, DFT calculation files, and other data are available through the University of Cambridge Apollo repository; supplementary information is linked from the article.
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