Title: Atomic Diffusion Models for Small Molecule Structure Elucidation from NMR Spectra
Venue: NeurIPS 2025
Accepted: yes
Keywords: Diffusion Models

NMR Spectroscopy

Natural Products

Machine Learning for Chemistry
Abstract: Nuclear Magnetic Resonance (NMR) spectroscopy is a cornerstone technique for determining the structures of small molecules and is especially critical in the discovery of novel natural products and clinical therapeutics. Yet, interpreting NMR spectra remains a time-consuming, manual process requiring extensive domain expertise. We introduce ChefNMR (CHemical Elucidation From NMR), an end-to-end framework that directly predicts an unknown molecule's structure solely from its 1D NMR spectra and chemical formula. We frame structure elucidation as conditional generation from an atomic diffusion model built on a non-equivariant transformer architecture. To model the complex chemical groups found in natural products, we generated a dataset of simulated 1D NMR spectra for over 111,000 natural products. ChefNMR predicts the structures of challenging natural product compounds with an unsurpassed accuracy of over 65%. This work takes a significant step toward solving the grand challenge of automating small-molecule structure elucidation and highlights the potential of deep learning in accelerating molecular discovery.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nNMRDiff3D is proposed to predict the 3D structure of molecules from 1D NMR spectra. The model uses a transformer encoder over a 1D convolutional layer to predict 3D atom coordinates. Diffusion is used as the training strategy, and data augmentation is applied to learn SE(3) equivariance by scaling up. Results show that NMRDiff3D outperforms previous methods on synthetic and experimental datasets.\n\nSTRENGTHS AND WEAKNESSES:\nI congratulate the authors. I truly enjoyed reading the work.\n\nOriginality: \n- First approach that uses diffusion to predict 3D coordinates from 1D NMR spectra. Diffusion is a well-motivated choice for this task due to its recent success in 3D generation.\n- The developed deep learning architecture is a well-motivated architecture and fits the data modality.\n- Learning equivariance at scale, despite not being a novel idea, is a good choice to simplify the model.\n\n\nClarity:\n- Very well-structured and written paper. It was easy to follow top to bottom, although I am not an expert in NMR spectroscopy.\n\nQuality:\n- Experimental pipelines are well thought out and executed. \n\n\nSignificance\n- The performance gain over the benchmark is significant.\n- It can replace the existing methods for 3D structure prediction from 1D NMR spectra. I cannot estimate the size of this potential, though, due to my background.\n\n\n**Weaknesses**\n\nI believe the work is already in good shape. I have several concerns about the applicability domain and evaluation of the model. I will happily update my grades if these concerns are addressed.\n\nQuality\n\n1. (L260) The model size indeed increases the performance, but a big performance boost is already observed with NMRDiff3D-M. This can be emphasized. I believe this would help readers appreciate the quality of the model and the ideas behind it better. Authors can also change the naming to NMRDiff3D-S vs NMRDiff3D-L, as \"M\" naming hints at the existence of an \"S\" model, which is not the case.\n2. The authors generate the synthetic data via MestreNova. However, the applicability of this method to generate 3D structures of natural products is not discussed.\n3. Stereochemistry is removed when generating the dataset. However, the studied task is in 3D by nature, and natural products are *known* to have rich stereochemistry. This can make the evaluation task artificially easier.\n\nSignificance\n1. The structural and 3D similarity between the train and test splits is not reported. This is very important to measure the generalization of the model, as well as its real-world applicability.\n\nClarity\n\n1. (L279) The ablation study discussion can be revisited to add more nuance and emphasize the magnitude of the effects. For instance, not all ConvFormer components are equally important (some cause only a 1% drop in accuracy), and this can be highlighted. The comparison to the patch tokenizer can be moved to the main text from the appendix.\n2. Not a weakness, but a suggestion: authors can list their key contributions at the end of the intro, as this is typical in NeurIPS papers and helps the reader to follow the paper.\n\nQUESTIONS:\nI have questions linked to my concerns above. I would be happy to increase the grades if these questions are answered.\n\n\n1. Can you comment on the accuracy of the synthetic data generation? How reliable is the used MestreNova software for natural products? Is there a way to verify the generated structures?\n2. Why did you remove stereochemistry from the dataset? How does it affect the evaluation of the model?\n3. Can you report the 2D and 3D structural similarity between the train and test splits? If these splits are similar, can you run experiments on distant splits to reveal the generalization of the model and the benchmarks?\n4. How long does it take to train one NMRDiff3D model?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-11"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces NMRDiff3D, a hybrid convolutional transformer and 3D atomic coordinate diffusion model for predicting small molecule structures directly from 1D NMR spectra and chemical formula, achieving state-of-the-art results on both synthetic and semi-real benchmarks.\nThe authors also present SpectraNP, a large-scale synthetic NMR dataset tailored for natural products, which fills a critical gap in available training data, especially on more complex (i.e., >101 atoms) molecules.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n- NMRDiff3D achieves state-of-the-art performance on synthetic and semi-real benchmarks and shows consistent prediction quality across a wide range of molecular sizes. \n- The paper includes ablation studies isolating the contributions of individual components and attempts to evaluate results on experimental data, although this evaluation is very limited in scale.\n- While the model does not handle stereochemistry, this aligns with the protocol followed by prior work (Hu et al., 2023) and is therefore appropriate given the benchmark comparison.\n\nWeaknesses: \n\n- The paper claims that joint training on USPTO and SpectraNP demonstrates generalization across chemically dissimilar datasets. However, this claim remains insufficiently substantiated due to the absence of scaffold-level or fingerprint-based diversity analysis; the fact that only 306 molecules are shared between USPTO and SpectraNP does not preclude significant scaffold or chemotype overlap that could lead to memorization rather than true generalization. As it stands, there paper lacks: (1) Bemis-Murcko scaffold overlap analysis to assess shared core structures and rule out memorization; (2) dissimilarity-based splits or stratified performance metrics to evaluate generalization to unseen scaffolds; and (3) fingerprint similarity statistics or distribution plots (such as Tanimoto similarity histograms of ECFP4 / MACCS keys) between datasets to quantify molecular diversity. Without these, it is unclear whether performance gains arise from genuinely transferable representations or interpolation within familiar chemotypes, undermining the central generalization claim.\n- The lack of a systematic categorization of failure types (such as incorrect functional groups or misconnected substructures), or failure mode analysis based on specific compound classes (such as halogenated molecules, sugars, large rings), prevents understanding the boundaries of the model’s reliability and might inflate generalizability claims (like performing well on average classes but failing systematically on important ones). There are also no metrics quantifying the domain shift from synthetic to experimental spectra. A calibration of prediction confidence (e.g., likelihood of correctness across different noise levels, so an evolution of the general confidence score cited in Future Works in the Conclusion) and measurement of accuracy degradation due to factors like solvent noise, peak shifting, or missing atoms would also be appreciated.\n- While the authors do state that stereochemistry was excluded from their input/output space and note that Alberts et al [3]. included it, they fail to clearly articulate that this renders the tasks non-equivalent. Including stereochemistry significantly increases the complexity of the structure elucidation problem, and this difference should be explicitly highlighted to avoid misleading comparisons.\n\nQUESTIONS:\n- Can you provide scaffold-level and fingerprint-based diversity analyses to substantiate the generalization claim, and have you evaluated performance on a scaffold-based split or filtered out scaffold overlaps during testing?\n- Can you provide a systematic failure mode analysis, especially on experimental spectra?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces NMRDiff3D, a diffusion based framework to predict the 3D structure of organic molecules conditioned on the 1D NMR spectra (both H and C spectra) and the chemical formula. The model was trained on a curated set of synthetic data, which is around 100k in size. The paper introduces two metrics about accuracy and similarity and compares their method to two LLM based methods and one graph based method.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n- The paper is well written and is of sufficient quality for NeurIPS \n- This is a very interesting/relevant problem that not a lot of people focus\n- As far as I can tell (I have not specifically worked on this problem), the paper presents a novel diffusion based approach to the inverse problem of spectra to structure\n\nWeaknesses:\n\n- While the idea is novel the pieces used are not, it would be good to clarify what required new thinking vs what was putting together existing pieces\n- It is difficult to assess how good/useful the method actually is, the baselines seem quite weak.  \n- The paper emphasizes a scalable non-equivariant approach but are training on small datasets < 1M in size, being non-equivariant is fine but in the small data regime it might actually be beneficial to enforce symmetries because those models have been shown to be very data efficient. Interesting to note that coord augmentation had the largest impact out of all ablations shown table 3.\n\nQUESTIONS:\n- Can you clarify the technical contributions to the model in the introduction (i.e. what components needed to be changed for this specific problem)?  \n- If you are generating 3D structures (and have loss terms on the coordinates) why is there no RMSD metric on the atom positions?\n- How generalizable is the method across different atom types and different solvents? Are there limitations here? \n- Can you elaborate on why graph methods are limited to 64 atoms?\n\nLIMITATIONS:\n- Are there better ways to quantify the performance of the method? Can you can compare to any physics based methods or methods that are currently used by the community? If that isn't possible, what is level of accuracy needed to see this method widely adopted? There are lots of reasons to like this paper but without more context/clarity on the impact, it is hard to recommend accept.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a novel, state of the art approach to generating the complete structure of a small molecule using a diffusion model from 1D NMR (Carbon and Hydrogen) experiments and the molecular formula. They outperform a previous model as well as a version of their model that generates graphs. The model is quite promising for aiding biochemists in structure determination from these measurements. They also present a new training dataset, SpectraNP, of predicted 1D NMR measurements generated by MestreNova. So the model is trained with simulated spectra, but they then test it on experimental data with excellent results.\n\nI have read the authors' response, and I am satisfied to keep my score as is.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\nQuality\n\nThis is a high-quality paper. The model is fairly clearly described, the results are very good, they even provide an alternative baseline model that generates graphs rather than 3D coordinates. Their model outperforms Hu et al. and Alberts et al. often by very large amounts (e.g., 86.6 vs. 73.4 Top-1 accuracy on UCPTO). \n\nClarity\n\nThe approach is well-described. They will release their code when the paper is published.\n\nSignificance\n\nThis is a tool that would gain significant usage by the Natural Products community, if it was posted on the web with a user-friendly interface. Is there a plan to do that?\n\nOriginality\n\nThere exist models that are end-to-end for 1D NMR that predict structure (their reference 20), but doesn’t use diffusion.\nThere also exist models that use diffusion to generate 3D structure (their references 19, 44, 65, 38)\nbut don’t generate molecules conditional on some data, like here, 1D NMR + chemical formula.\nSo, AFAIK, this is the first model to use 1D NMR plus molecular formula to conditionally generate molecular structure. \n\nWeaknesses: \n\nThe paper claims to have created the first and largest 1D NMR database, with over 100k computed spectra: \n\"To scale to the complex chemical groups found in natural products, we curate SpectraNP, a large-scale dataset of synthetic 1D NMR spectra for 111,193 complex natural products (up to 274 atoms), significantly expanding the chemical complexity of prior datasets (≤101 atoms) [20, 4].\"\n\nThere are at least two datasets that belie this claim:\n\n1. Your reference [4] has 790k molecules, but (with respect to your claim) only molecules with fewer than 35 heavy atoms were included. You mention 274 atoms, but how many heavy atoms were included?\n\n2. Your reference [60], NP-MRD has 200k 1D spectra, and is available to the public. As one example, NP-MRD provides the predicted 1D NMR spectra of QS-III (C104H168O55), which has 327 atoms. So this seems to have more molecules $and$ a larger number of atoms. \n\nIs there any advantage of SpectraNP over either of these?\n\nIt would be relatively easy to get even more NP structures and simulate them from https://coconut.naturalproducts.net/ which has nearly 700k 2D structures of natural products, so this would be a good source for expanding your dataset. \n\nThe assumption of molecular formula from mass spec: This is an NP-Hard problem, and the larger the molecule, the more intractable it gets to compute the formula. Anything over 200 heavy atoms (neglecting hydrogen) gets difficult, although the vast majority of natural products are below that (in coconut, at least). \n\nPresumably, MestreNova has some limitations on the use of its software. Is it within the user agreement to make public a dataset of over 100k computed spectra (for each of 1H and 13C) using their software?\n\n\nMinor comments/typos/wordsmithing:\n\nEquations 1-3: I’m unclear how this enters into the modeling. Why aren’t you just entering the peak coordinates directly, since they are in the data?\n\nSection 3.2: Pardon my ignorance; I don’t work with diffusion models (yet!). Presumably the output is a certain (fixed) dimension, right? (Correct me if I’m wrong). Do you simply have a maximum dimension and just 0-pad it? \n\nLine 134: Are these thresholds annealed during training?\n\nLines 143-144: You should include these (scaling functions) in the appendix so the paper is self-contained. \n\nFig 4: Is NMRDiff3D-L covered by the curve for NMRDiff3D-M in the middle and right hand graph?\n\nQUESTIONS:\nPlease answer any questions that arose in my review, thanks!\n\nLIMITATIONS:\nThe only obvious limitation is the training set; a larger dataset would lead to better results. \n\nAlso, I wonder if MestreNova would object to the public release of a dataset of over 100k molecules' predicted 1D NMR (H & C) that was computed with their commercial software?", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this paper, the authors tackle the challenge of predicting the chemical structure from the combined 1H and 13C-NMR spectra. To this end the authors develop a hybrid diffusion framework consisting of a Transformer based on encoder (with the spectra ingested via patches) and a diffusion transformer generating the molecular structure. Both 1H and 13C spectra are passed into the model as 10,000 dimensional vectors and the molecule is generated as a 3D structure. The authors train and evaluate their framework on synthetic data as well as perform evaluation on experimental spectra. Across all experiments the authors model outperform baselines.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- Very pertinent topic with high impact on chemistry\n- Novel modelling approach\n- Extensive ablations\n\nWeaknesses:\n- Limited experimental evaluations and benchmarking against existing models\n\nQUESTIONS:\n- The authors modelling approach should work equally well when only applied to 1H or 13C NMR spectra. Is there a reason why only the combination of 1H and 13C was considered? Training the model on only one type of spectra would make benchmarking easier while making the paper more convincing.\n\n- The paper would highly benefit from further benchmarking on experimental data as well as an expanded discussion of the performance on experimental data. Both [1] and [2] evaluate their modelling approaches on experimental 13C data from the nmr db2 [3]. While this database only provides peak positions the data could be easily converted into vector form. [4] also shows performances on the van Bramer dataset and may prove an interesting model to benchmark against.\n\nI am willing to increase my score if my concerns above are sufficiently addressed.\n\n[1]: J. Phys. Chem. Lett. 2022, 13, 22, 4924–4933\n[2]: Jonas, NeurIPS 2019\n[3]: https://nmrshiftdb.nmr.uni-koeln.de/#:~:text=nmrshiftdb2%20is%20a%20NMR%20database,spectra%2C%20structures%20and%20other%20properties.\n[4]: Alberts 2025, DOI: 10.26434/chemrxiv-2025-q80r9\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}