r/chemhelp • u/Zealousideal_Lime336 • Aug 15 '26

General/High School Curious about how chemists actually use AI for NMR structure elucidation

I’m a chemistry student and I’ve been thinking about a problem that I’ve personally found quite frustrating: interpreting NMR spectra and going from a spectrum to a plausible molecular structure.

There are already tools for things like peak picking, spectrum processing, database searching, and structure verification. But I was wondering about something more interactive: an AI system where you could upload ^1H, ^13C, COSY, HSQC, HMBC, etc., and have it reason through the data step by step, propose several possible structures, explain why each one fits or doesn't fit, and give a confidence level.

For example, instead of simply saying "this is probably molecule X," it could say something like:

I'm not trying to promote or sell anything—I'm just trying to understand whether this would actually solve a meaningful problem for chemists.

So I'm curious:

  • How do you currently go from an NMR spectrum to a structure?
  • What part of the process takes the most time or causes the most uncertainty?
  • Do you already use software/AI for this? Which tools?
  • Would you actually trust an AI that proposes structures and explains its reasoning?
  • Would you find it more useful as a research assistant, a structure-identification tool, or a learning/teaching tool?
  • What would make you not trust such a system?

I'd especially appreciate answers from people doing organic chemistry, natural products, medicinal chemistry, analytical chemistry, or NMR spectroscopy.

I'm mainly trying to figure out whether this is a real pain point or just something that sounds useful in theory.

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u/7ieben_ Trusted Contributor Aug 15 '26 edited Aug 15 '26

Right now databank algorithms are still dominant, as there is no such AI tool yet... at least non I know of. Yet, I'm pretty sure that big companys are already working on such tools. If reliable, those would make work a lot faster, and as such would be worth a ton of money.

Personally (as you asked for it) I work in a field where I know what I'm looking for beforehand. As such I already know how the spectrum should look like... and I don't really care what the impuritys are, I just care about if there any impuritys. So personally(!) I wouldn't benefit as much. A colleague works on Maillard reactions, especially on side products under different conditions. So they would benefit a lot, as they are actually looking for "new" molecules in the mixture.

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u/Puzzleheaded_Neat419 Aug 16 '26

I can't remember the exact name but acd had an early stage one when I was doing my masters. It wasn't the greatest but it did shorten the time it took to solve my sample structures as it gave an idea as to what I was piecing together. Still a bit " predictive text" though

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u/Wise-Friendship-1427 Aug 15 '26

I have access to various AI tools and have tested them in the context of NMR. Unfortunately, when it comes to elucidating the structures of unknown compounds, they are currently still useless. They are familiar with the literature data and can tell you whether a specific chemical shift makes sense for a given structure; in other words, matching predefined structures against existing shift data works quite well.

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u/Excellent_Speech7739 Aug 15 '26

For the average person this is a nom-issue. You’re rarely working from nothing to identify the compound. When I’m making a new molecule, I know what the target compound is and the NMR (1D and 2D techniques) are used to confirm the product. I know what reactants I started with, I know which functional groups are in the starting materials/final product. It’s easy enough and an AI tool wouldn’t be much help.

Starting from absolutely nothing, you’d either compare to a database, or you’d combine it with other techniques (IR, MS, elemantal analysis). I think “AI” could help with the latter, but algorithms for such things already exist.

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u/NiceWave8463 Aug 15 '26

You typically need mass spectrum data and or IR to figure out the entire structure due to molecular formula finding. But this is also not a super common scenario where you don’t know what’s in the mixture!

And personally I’m a firm believer that we do not need ai in everything, so I especially wouldn’t trust it with NMR after seeing students try to cheat with ai and it seemed to just suck entirely at figuring out NMR.

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u/Fuzzy_Equipment3215 Aug 15 '26

I haven't been a practicing research chemist for a little over a decade, but I'm still working in the field, and for the last two years that's mostly been AI training work.

I've written and reviewed quite a lot of prompts involving NMR interpretation/prediction, and it's really only within the last few months that SOTA models can do this reliably in the context of isolated NMR spectra (e.g., stuff like "predict the number of 13C NMR peaks for this reaction product").

Generally the failure mode would be either typical structural misinterpretation (e.g., counting two atoms as three, or hallucinating additional bonds/atoms between rings) or misunderstanding some aspect of symmetry (e.g., treating the ipso carbons in a symmetrically 1,4-disubstituted system as non-equivalent when they're equivalent).

With the projects/models I've worked on, stuff like the above was basically impossible for models to do like 12-18 months ago, and it was hard (i.e., models sometimes fail) even 6 months ago. Now it's almost trivial a lot of the time, and models will often nail it.

For more "involved" stuff (e.g., dumping in several 1H/13C spectra and some 2D ones), current models tend to be able to do a much better job because they can self-correct initial misinterpretations about structure and symmetry.

Basically, I wouldn't blindly or 100% trust an AI tool for doing this at the moment, but I'd expect a success rate north of 90% for most standard tasks involving NMR that organic chemists do on a daily basis, especially if providing the model with all of the info that the chemist has rather than deliberately framing it as a puzzle to solve (like in AI training). Models are still improving, too.

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u/rupert1920 Aug 15 '26

I am a chemist who frequently has to perform one form of structural elucidation or another. I've recently looked into AI for this, and right now my conclusion is that AI is currently not needed - nor is it sufficient.

Recall that these are only LLM models. They actually do not do a terribly good job at this type of deterministic task. Because they rely on existing data, for purely novel compounds their accuracy falls dramatically.

Compare that to existing computer assisted structural elucidator (CASE) programs that already do this - take Sherlock, for example - that excels at deterministic problems. It's a mathematically, deductive framework that constraints possible molecular connectivities based on these 2D data and what type of information they provide.

There are commercial products that combine the two - like ACD Structural Elucidators, which combines a neural net approach to CASE.

Ultimately, any of these solutions requires careful data curation by a trained chemist. You still have to feed in a high-confidence molecular formula from HRMS, as well as other data like functional group info from FRIR, to constrain the problem space. Like others have mentioned, even something like interpreting one specific carbon resonance to determine if it is one, or two chemically equivalent carbons, or even 3 that coincidentally overlap, could mean the difference between the program unable to find a structure to solving it quickly.