The question of determining the actual chemical structure underlying a sensorially perceivable substance, such as the product of a chemical reaction, is a core challenge in chemistry. Analytical chemistry provides a number of techniques to address this problem. Amongst those nuclear magnetic resonance (NMR) spectroscopy is one of the most informative. Each NMR spectrum is characteristic of a specific molecular structure, as different structures produce different spectra. Skilled spectroscopists can infer a structure from spectra, a process known as structure elucidation. For in-depth details of NMR spectroscopy and structure elucidation methodologies, we refer the reader to Elyashberg (2015) and the bibliography there.

The ambition to automate the process of structure elucidation emerged as soon as computer systems became available in chemistry. Software created for this is known as computer-aided structure elucidation (CASE) software (Kuhn et al. 2025). Major developments in the area included COCON (Lindel et al. 1997), SENECA (Kuhn and Steinbeck 2010), LSD/pyLSD (Nuzillard 2003), Mestrelab MNova (MestraLab 2024), Bruker CMC-se (Kessler and Godejohann 2018), ACD/Structure Elucidator (Elyashberg et al. 2004 and Sherlock (Wenk et al. 2023). NMRFilter (Kuhn et al. 2019, Kuhn et al. 2020) is an example of compound annotation in mixtures. Quantum calculations-aided structure elucidation also offers high-precision insights by simulating NMR parameters directly from first-principles (Costa et al. 2021), but those methods will not be addressed here. Those programmes typically compare candidate structures to the spectral data measured using spectrum prediction. Given the vastness of chemical space, they typically use heuristics (e.g. genetic algorithms or simulated annealing) to restrict the search space. Further constraints (e.g. known functional groups) are used to retrieve a result as unambiguous as possible. Nevertheless, the outcome is frequently a ranked list of candidate structures rather than a definitive solution. Given the recent significant successes achieved by artificial intelligence systems (for example, the essays produced by large language models, Binz et al. (2025)), it is a natural question to ask if this task could be performed by an AI system without the explicit steps contained in CASE systems (Kuhn et al. 2025). Whilst, to our knowledge, there is currently no such complete system, steps in the direction have been taken. One example is Hu et al. (2024), which predicts substructures from 1D spectra and assembles these into structures. The DeepSAT system (Kim et al. 2023) combines structure generation with database searches. As opposed to the directed generation of compounds to find one that matches the measured spectra best, DeepSAT predicts a fingerprint and a chemical class from the spectra using machine-learning techniques and searches a database for the best fit to those predicted properties. In Kuhn et al. (2022), it was demonstrated that a neural network can tell if a certain fragment is contained in a structure or not from an NMR spectrum. This work uses two-dimensional experiments, namely Heteronuclear Single Quantum Coherence (HSQC) and Heteronuclear Multiple Bond Correlation (HMBC). They show 1-bond and 2/3-bond 13C-1H shift correlations and, therefore, contain a significant amount of structural information. This neural network can be viewed as a step towards data-driven automated structure elucidation.

Whilst ML methods hold significant promise, there are downsides as well. Chief amongst these is their "black-box" nature, as models often produce predictions without providing insight into the underlying reasoning. They produce a result, but there is no explanation given as to why or any visible reasoning. Not only is this unsatisfying for the user, but it also makes users distrust the results. Hence, explainable AI is a critical development. This paper shows some attempts to make the results from Kuhn et al. (2022) explainable. To the best of our knowledge, this represents the first attempt to incorporate explainable AI into the context of NMR-based structure elucidation.

To evaluate the effectiveness of Class Activation Mapping (CAM) methods and neural networks in chemical structure elucidation, a series of heatmap images were generated. The evaluation focuses on comparing the heatmaps of pure substances with those of artificially created mixtures that include the original pure substances. These artificial mixtures are produced by overlaying spectra from the three target compound classes - steroids, fatty acids and indoles - with spectra from compounds that do not belong to any of these subclasses. Each artificial mixture will include spectral data from one of the target classes along with one or two additional spectra from unrelated compounds. This method enables the assessment of the models’ ability to accurately classify relevant substructures and identify the substructure region in the spectra using Grad-CAM and Grad-CAM++ algorithms.

From our observations, we can draw a number of conclusions about the use of CAMs in structure elucidation.

One observation is that, at least Grad-CAM and Grad-CAM++, do not provide the level of detail which would be needed to identify peaks. This makes the ideal goal of this exercise a target we have not reached.

On the other hand, the heatmaps produced show that, for pure compounds, relevant parts of the spectrum are considered by the networks. On the other hand, when looking at the mixtures, the picture is not clear - in many cases (but not all), parts of the compounds which should not contribute, are used as well. This confirms the results of Kuhn et al. (2022), where fragments were identified reliably in pure compounds, whereas mixtures often failed.

Due to this, heat maps, even if not ideal for determining peaks, can help to evaluate a model beyond the usual metrics. As we have seen, using Grad-CAM, we better understand what is going on in the model than without it. In so far, this can serve as a step towards explainable AI. It should be noted that the basis for the application is the use of spectral images and convolutional neural networks, which are commonly used for image analysis.

We have demonstrated that a class activation map, particularly Grad-CAM, can help better understand the results of analysing spectra using neural networks. Whilst we did not achieve our ideal goal, we have demonstrated that the previous results become better understandable and easier to interpret. It is also clear that better models are needed for the mixtures. In general, finer-grained methods than Grad-CAM would be helpful. Finally, more data will help establish a broader base for analysis.