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World desk3 min

How Infrared and NMR Together Improve Automated Structure Verification

A 2025 benchmark found that combining IR and proton NMR improved automated ranking of closely related candidate structures, but the method still depends on candidate lists and human review.
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Combining infrared (IR) and proton nuclear magnetic resonance (NMR) data improved how well an automated method ranked closely related candidate chemical structures in a 2025 benchmark. The method helps verify proposed structures against measured spectra; it does not generate a molecule from spectra alone, and the study’s authors say human interpretation remains necessary.

What automated structure verification does

Automated structure verification (ASV) starts with candidate structures and tests how well each fits experimental data. That is different from unconstrained structure elucidation, where an analyst must infer a complete structure without a predefined candidate list.

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In the 2025 study by Rowlands and colleagues, the system ranked a correct structure against a closely related incorrect isomer. The benchmark covered 42 drug-like compounds and 99 comparisons. Its results therefore describe this constructed set of candidate comparisons—not a general accuracy rate for unknown molecules or routine laboratory samples. Read the study in Chemical Science via PubMed Central.

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Why combine IR and proton NMR?

IR contributes evidence about bond vibrations

Infrared spectroscopy measures absorptions associated with molecular vibrations. The resulting spectrum includes a fingerprint region that can help distinguish structures with similar compositions.

NMR contributes atom-focused evidence

Proton NMR chemical shifts provide evidence about the environments of hydrogen atoms. Because IR and NMR reflect different aspects of a molecule, their scores can complement one another when two candidate isomers are hard to distinguish using only one modality.

How the combined ranking works

Rowlands and colleagues introduced IR.Cai to compare experimental IR spectra with calculated spectra. They paired it with proton NMR scoring, including DP4*, a modified approach that excludes outlying shifts associated with exchangeable protons that are difficult to predict.

The method does not directly add together the raw IR and NMR scores, which use different scales. Instead, it converts each modality’s candidate scores into percentile ranks and averages the ranks. A candidate ranks strongly overall when it performs well across both kinds of evidence.

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The comparison also included ACD/Labs ASV, a commercial NMR-based comparator. The paper reports improved comparison performance when IR was combined with NMR; combining two scores from the same modality did not show the same improvement in the reported controls.

Rank #3

What the benchmark found

The study classified comparisons as correct, incorrect, or unresolved. A score-difference threshold sets how confidently the method must favor one candidate; a stricter threshold can maintain a chosen true-positive rate by leaving more ambiguous pairs unresolved.

Selected true-positive rate Combined IR and NMR: unresolved pairs Individual techniques: unresolved pairs
90% 0–15% 27–49%
95% 15–30% 39–70%

These ranges are reported for the study’s candidate comparisons. They show a trade-off between the selected true-positive rate and how many pairs the method leaves undecided; they are not universal rates for other compounds or laboratory workflows.

Rank #4

For high-level IR calculations, approximately 73% of pairs were solved at a 90% true-positive rate. Adding IR to DP4* NMR scores raised that figure to 85%, while adding IR to ACD NMR scores raised it to 100%. Across the challenging dataset, the authors report that combined IR and NMR solved all potential comparisons at an 85% true-positive rate, with a classification-area (CA) score of 0.966.

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Important limits and practical context

Performance depends on the comparison set

The authors caution that relative performance between modalities depends on the test set. These results support combining complementary evidence for the specific benchmark, not a claim that fusion will always improve results by the same amount.

Some pairs remain unresolved

An unresolved result is an intentional outcome when the evidence does not clear the threshold for a confident classification. The system is not designed to force a decision for every pair at every operating point.

Calculation and spectral conditions matter

The study says density functional theory (DFT) calculations are currently needed to simulate NMR and IR spectra. For its IR calculations, it used the 1250–1600 cm⁻¹ range because DMSO-d6 strongly absorbs near 1100 cm⁻¹ and extending the range higher did not improve results in this dataset. That choice is specific to this study, not a universal rule for IR analysis.

Human review remains part of interpretation

The authors state in their abstract: “Whilst there have been advances in automated spectral interpretation, the false positive and false negative rates remain too high to replace human interpretation.” The results position ASV as decision support for comparing candidates rather than a substitute for expert review.

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Data and study disclosures

The authors say recorded IR and NMR spectra and DFT calculation files are available through the University of Cambridge Apollo repository; supplementary information is linked from the article. The paper also reports author relationships with AstraZeneca, including employment and shareholding for several authors and funding support for the work.

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