Pith. sign in

REVIEW 4 major objections 4 minor 67 references

The paper claims that NMR structure elucidation is better framed as an agentic, constraint-driven search than as an end-to-end mapping, and that a frozen LLM with chemistry tools can match graduate students on real spectra.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

T0 review · deepseek-v4-flash

2026-08-02 08:10 UTC pith:SDVRCXT2

load-bearing objection Solid demonstration of an agentic NMR elucidation harness, but the headline accuracy claims don't survive the memorization objection; the search-based gains are not yet established. the 4 major comments →

arxiv 2607.19406 v1 pith:SDVRCXT2 submitted 2026-07-07 cs.LG

NMR Elucidation as an Agentic Search Problem, Not a Modeling Problem

Irina Espejo Morales , Damon Hinz , Marvin Alberts , Geraud Krawezik , Haewon Jeong , Shirley Ho This is my paper
classification cs.LG
keywords NMR structure elucidationagentic searchfrozen LLMconstrained generationspectral preprocessingself-verificationsmall moleculeschemistry automation
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper argues that small-molecule structure elucidation from routine 1D NMR spectra is best treated as a search problem, not an end-to-end modeling problem. The authors build a single autonomous agent driven by a frozen large language model, equipped with deterministic spectral-processing tools, structured evidence tables, and self-verification checks. Without any training on spectra, simulation of datasets, seed structures, or internet access, the agent proposes candidate molecules from the raw data and checks them against the molecular formula, degree of unsaturation, proton counts, and symmetry. On a 15-molecule expert-evaluated set it reaches 71% top-1 accuracy, comparable to graduate students at 66%; on a clean educational set it reaches about 80%, and on a hard drug-like set about 20%, beating zero-shot deep-learning models trained on simulated spectra. A sympathetic reader would care because the result suggests a training-free, interpretable route to automating routine structure elucidation.

Core claim

The paper's central claim is that an LLM-guided constrained search can replace learned spectrum-to-structure mapping. An agent receives raw free-induction-decay files and a molecular formula, processes them through a deterministic pipeline into peak lists and an EvidenceTable, classifies likely functional groups into an EnvironmentTable, generates candidate SMILES structures from scratch, and then verifies each candidate against data constraints rather than against ground truth. The verification layer—exact formula match, symbolic degree of unsaturation, proton-integral accounting, and chemical-environment counts from graph symmetry—is what turns open-ended generation into a bounded search.

What carries the argument

The load-bearing machinery is the verification loop around a frozen LLM. First, a deterministic preprocessing pipeline converts vendor NMR files into peak lists and an EvidenceTable of shifts, integrations, and solvent peaks. The LLM then builds an EnvironmentTable of candidate functional groups, proposes candidate molecular graphs, and must pass each proposal through hard checks: exact molecular formula, symbolic degree of unsaturation, consistency of 1H integrals with the formula, and agreement between the number of distinct 1H/13C environments and the topological symmetry of the proposed graph. This loop constrains generation and provides a stopping criterion, so the system's performance

Load-bearing premise

The load-bearing premise is that the frozen LLM's correct answers come from reasoning over the spectra and constraints, not from having memorized the benchmark molecules and their spectra during pretraining; if memorization dominates, the reported accuracy measures recall, not agentic search.

What would settle it

Benchmark the agent on a set of molecules whose 1H and 13C spectra were not publicly available before the LLM's training cutoff, and compare top-1 accuracy with a matched set of pre-cutoff public molecules; if accuracy collapses on the withheld set while staying high on public ones, memorization rather than search drives the results. A complementary test: delete a diagnostic peak (e.g., the carbonyl in a peroxyacid) and see whether the agent identifies the resulting failure; the paper's own mCPBA case shows this can mislead both backbones.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

Share X Bluesky LinkedIn Reddit HN

If this is right

  • If the claim holds, routine small-molecule structure elucidation can be automated without collecting simulated training data or fine-tuning a model, using a frozen LLM plus hand-built chemistry tools.
  • The framework's accuracy scales with the quality of the environment—adding better preprocessing, stronger verification checks, and new modalities such as IR, mass spectrometry, or 2D NMR should improve candidates without retraining.
  • Because the agent produces explicit reasoning traces and tool calls, its proposals are inspectable and could help chemists prioritize structures for experimental confirmation.
  • The same agentic-search recipe may transfer to other spectral interpretation tasks where constraint checks are available, such as MS or IR structure elucidation.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The reported numbers may be inflated by pretraining memorization: if the benchmark molecules and their spectra were public before the model's training cutoff, top-1 accuracy could measure recall of memorized associations rather than genuine search. A fair test needs molecules whose spectra were published after cutoff.
  • The approach's failure on drug-like molecules suggests a ceiling: as spectra become noisier and candidates larger, the current verification rules may be too weak to prune the search space, so scaling may require new constraints, not just better prompts.
  • A testable extension of the paper's logic: vary which verification tools the agent can call and measure accuracy, which would directly quantify each constraint's contribution.
  • The environment-around-the-model perspective implies that progress in automated spectroscopy could be benchmarked as an environment-design problem, not a modeling problem, which would change how datasets for this task are constructed.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper proposes reframing small-molecule NMR structure elucidation as an LLM-guided constrained search rather than an end-to-end supervised mapping. A frozen LLM (GPT-5.4, Kimi K2.6, or Qwen3.5-122B) is equipped with tools that preprocess raw FID data, build an EvidenceTable, propose candidate SMILES, and verify candidates against molecular formula, degree of unsaturation, proton integration, and chemical-environment counts. The authors evaluate on three experimental datasets (van Bramer, Alberts, AstraZeneca) and report top-1 accuracies of 80%, 71%, and 20%, respectively, claiming parity with graduate-student performance on Alberts and superiority over zero-shot end-to-end models. The central claim is that the agentic environment, rather than the LLM alone, drives the gains.

Significance. If the central claim survives scrutiny, this is a meaningful contribution: it demonstrates that a frozen LLM combined with a small set of hand-coded chemical tools can partially automate routine 1D NMR structure elucidation without task-specific training or spectrum simulation, while producing interpretable reasoning traces. The paper is also unusually honest in documenting failure modes, including the mCPBA preprocessing corruption and the fenbufen near-miss, and it releases rich trace excerpts. However, the headline comparisons and the 'search not modeling' interpretation are currently undermined by a memorization confound, inconsistent evaluation protocols across baselines, and at least one documented preprocessing failure that directly affects the accuracy numbers. These issues are load-bearing, not cosmetic.

major comments (4)
  1. [Sec. 2.2, Sec. 3.1, Table 1] Section 2.2 explicitly allows that generated structures may come from 'information stored within the model,' and the evaluation sets consist of well-known compounds (anthracene, phenanthrene, diltiazem, fenbufen, mCPBA, 2,4,6-tribromoaniline) whose experimental spectra are likely in the pretraining corpora of GPT-5.4/Kimi K2.6. Thus the 71%/80% top-1 scores could measure memorized spectra–structure associations rather than agentic search. The coding-agent baselines (Table 1) show the environment matters, but they do not isolate the memorization component. Please add a contamination control: e.g., molecules published after the model cutoff, a no-tools baseline with the same LLM prompted directly, or a retrieval check in which the LLM is asked to name the compound from formula+peak list. Without such a control the central 'search not modeling' claim is not established.
  2. [Sec. 3.2, Table 1, Sec. 4.1] The headline comparisons are not supported by the numbers as reported. On Alberts (N=15 after removing the degraded sample), kimi-k2.6's 71.11±6.29 vs graduate students' 66.67±3.06 is within one standard deviation, with no significance test, and the 're-adjusted' published numbers need a described protocol. On AstraZeneca, gpt-5.4 achieves 7.84±1.39, below both the zero-shot Alberts model (14.70±4.20) and the Codex baseline (12.70±4.50), directly contradicting the abstract's 'outperforming zero-shot end-to-end deep learning models.' The van Bramer comparisons use different N and modalities across methods, as Section 3.2 itself notes. Please restrict claims to the models/datasets that actually support them and add statistical testing.
  3. [Sec. 4.3 (mCPBA case study), Sec. 2.1] The mCPBA failure is documented as caused by the preprocessing pipeline dropping the carbonyl carbon entirely (only two 13C peaks survive), and both backbones converge to the same wrong structure. Since preprocessing is part of the proposed environment, this is a load-bearing limitation: the reported accuracy on Alberts includes at least one sample whose input data were globally corrupted by the pipeline. Please report how many of the 15 Alberts / 34 AstraZeneca / 236 van Bramer samples had preprocessing failures (missing key peaks, failed phasing, etc.), state accuracy after excluding or fixing them, and discuss whether the claimed gains survive.
  4. [Sec. 3.2, Sec. 2.3] The verification rules are reasonable but encode only coarse constraints (formula, DoU, ±1 proton, environment counts); they are not a substitute for spectral simulation, and the paper does not quantify how many wrong candidates pass all checks. More importantly, the comparisons to prior work mix stereochemical handling: the paper always counts stereo errors as wrong while some baselines strip stereochemistry, as acknowledged in Sec. 3.2. Please provide a sensitivity analysis with stereo-stripped SMILES for the agent, or at least show the top-1 accuracy when enantiomer mismatches are counted as correct; this is necessary to compare against ChefNMR and other models.
minor comments (4)
  1. [Sec. 2.3, Sec. 4.1] Typos: 'formula fo' in Sec. 2.3; 'Irregardless' in Sec. 4.1. Please fix throughout.
  2. [Fig. 2] The stratified plots show noisy trends in bins with few molecules, and there are no error bars or confidence intervals. Adding pointwise CIs or overlaying individual data points would make the complexity trends more interpretable.
  3. [Table 1] The Claude Code baseline is a single run with very large standard deviations; clarify how many independent runs each reported mean and std is based on, and consider reporting per-run results in the supplement.
  4. [Overall] No code or data availability statement is given. Since the contribution is an environment and preprocessing pipeline, releasing code and raw FIDs (or a documented data-sharing plan) is important for reproducibility.

Circularity Check

0 steps flagged

No significant circularity: the agent's accuracy is measured against external benchmarks, and the pipeline's constraints are generic chemistry checks rather than fitted outputs.

full rationale

The paper's central claim is that a frozen-LLM agent with hand-built tools reaches competitive top-1 accuracy on external NMR elucidation benchmarks. I walked the derivation chain and found no step where a prediction is defined in terms of its own input, no fitted parameter is renamed as a prediction, and no load-bearing argument reduces to a self-citation. The preprocessing steps (FFT, phasing, baseline correction, peak picking) are deterministic and independent of the ground-truth structures. The verification rules—molecular formula match, degree of unsaturation, integration counts, and RDKit-computed chemical environment counts—are generic chemistry constraints and do not encode the benchmark answers. Accuracy is computed against external ground-truth structures from the van Bramer, Alberts, and AstraZeneca datasets, with human baselines taken from the original published sources; the only adjustment is re-scaling Alberts et al.'s reported accuracies from 16 to 15 molecules after excluding a degraded sample, which is a comparison-fairness step rather than a derivation. Self-citations to the Alberts dataset, AstraZeneca spectra, and MMST are references to independent published data and models, not to an unverified uniqueness theorem or ansatz. The memorization confound raised in the reader's take is a real validity risk for the interpretation of the results, but it is not circularity in the sense of the rubric: the paper does not define the agent's output in terms of memorized ground truth, and the benchmark comparison remains external. Therefore the derivation is self-contained against external benchmarks and receives a score of 0.

Axiom & Free-Parameter Ledger

3 free parameters · 5 axioms · 0 invented entities

No invented physical entities; components like EvidenceTable and validation tools are software constructs. The key uncharged inputs are the correctness of molecular formulas, the frozen LLM's pretrained chemistry knowledge, RDKit symmetry as a proxy for NMR equivalence, standard shift tables, and a peak-picking pipeline that preserves diagnostic signals. The only hand-tuned numbers are verification tolerances and preprocessing thresholds, which are not fitted to test labels.

free parameters (3)
  • Integration tolerance for 1H NMR validation
    Verification rule accepts candidates if observed integrals match formula protons within ±1 proton plus an allowance equal to the sum of N, O, S atoms (Section 2.3). This hand-chosen tolerance directly affects which candidates pass validation and thus accuracy.
  • Autophase acceptance threshold
    If the ACME autophase score falls below a threshold, a global grid search over phasing parameters is used (Section 2.1.2). The threshold is not derived from data and affects processed peak lists.
  • Peak-picking noise filter and Lorentzian fit constraints
    Peaks are selected using mean absolute deviation as a noise filter and fitted with Lorentzians (Section 2.1.2). These choices determine the evidence table the agent reasons over.
axioms (5)
  • domain assumption The provided molecular formula is correct for every benchmark task and is treated as a hard constraint.
    All candidate generation and validation depend on exact formula matching (Section 2.3). In real deployments formulas come from mass spectrometry and can be ambiguous.
  • domain assumption The frozen LLM's pretraining contains sufficient general chemical and NMR knowledge to propose plausible candidate structures.
    The agent receives no seed structures and no examples, so candidate generation relies on chemistry knowledge stored in the model (Section 2.2).
  • domain assumption RDKit topological symmetry classes correspond to the number of distinct 1H/13C NMR environments.
    The Chemical Environment Counts check compares proposed molecule symmetry against observed distinct peaks (Section 2.3); this equivalence can fail under accidental equivalence, dynamics, or overlap.
  • domain assumption Tabulated chemical shift ranges and splitting rules are reliable for the solvents and functional groups in these datasets.
    Reasoning excerpts rely on standard shift tables (e.g., acid carbon around 170-175 ppm). This prior knowledge is encoded in the knowledge documents described in Section 2.2.
  • domain assumption Preprocessing (phasing, baseline correction, peak picking) preserves all diagnostically relevant peaks.
    If a real peak is dropped or an artifact kept, the agent can be misled; the mCPBA case study shows the true carbonyl is absent after solvent removal.

pith-pipeline@v1.3.0-alltime-deepseek · 15072 in / 13144 out tokens · 117911 ms · 2026-08-02T08:10:50.028232+00:00 · methodology

0 comments
Cite this review

Pith. "Pith review of NMR Elucidation as an Agentic Search Problem, Not a Modeling Problem." pith.science (2026). https://pith.science/paper/SDVRCXT2

@misc{pith2026260719406,
  author       = {Pith},
  title        = {Pith review of: NMR Elucidation as an Agentic Search Problem, Not a Modeling Problem},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SDVRCXT2}},
  note         = {Machine review of arXiv:2607.19406}
}
Share X Bluesky LinkedIn Reddit HN
read the original abstract

Structural elucidation from Nuclear Magnetic Resonance (NMR) data remains a fundamental bottleneck across chemistry, materials science, and biology. We demonstrate that an agentic AI system can perform this task at a level comparable to graduate-level chemistry students. Instead of training a model to directly map spectra to structures, we build a single autonomous agent, backed by a frozen LLM, that interacts with a curated environment with access to domain-specific processing tools, validation checks, tabulated chemical shifts, and instructions that outline the stepwise nature of a chemist's thinking process. On the Alberts dataset, our agent elucidates structures with a top-1 accuracy of 71%, comparable to the performance of graduate students at 66% top-1 accuracy. On the van Bramer and AstraZeneca datasets, our agent achieved 80% and 20% top-1 accuracy respectively, outperforming zero-shot end-to-end deep learning models which were trained on large datasets of simulated spectra. These results show that reframing NMR elucidation as an LLM-guided constrained search, rather than a modeling task, yields substantial gains and suggests a path toward multi-step orchestration frameworks that integrate a variety of tools, models, and domain knowledge to assist in automating spectroscopic analysis.

Figures

Figures reproduced from arXiv: 2607.19406 by Damon Hinz, Geraud Krawezik, Haewon Jeong, Irina Espejo Morales, Marvin Alberts, Shirley Ho.

Figure 1
Figure 1. Figure 1: Overview of NMR data pipeline for proposing molecular candidates. The proposal pipeline is broken into four different phases: (1) Data Ingestion and processing - Ingesting the raw data and transforming it into human readable forms. (2) Analysis and data organization - Perform peak deconvolution and analysis of peaks, then generate data tables to use for reasoning about the data. (3) Reasoning - Reason on t… view at source ↗
Figure 2
Figure 2. Figure 2: Stratified evaluations by selected molecular properties. Performance on the Chemistry Education dataset for kimi-k2.6 stratified by (A) Heavy-atom count and (B) Number of unique 13C environments. Green lines show the fraction of tasks within a bin where the agent was correct. The faint gray histogram shows the total number of tasks per bin. Performance tends to decrease with the number of unique carbon env… view at source ↗
Figure 3
Figure 3. Figure 3: Case studies of top-1 predictions by our agent. All examples are predictions from kimi-k2.6. Top: examples that were predicted robustly, with the agent returning the same answer in all or nearly all queries. (A) The agent correctly disambiguates two structurally similar isomers with the same molecular formula and never confuses them with each other. (B) Across a set of varied molecules, the agent predicts … view at source ↗
Figure 4
Figure 4. Figure 4: Additional correct predictions by our agent. Examples of correct top-1 predictions from kimi-k2.6. 11 [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Additional incorrect predictions by our agent. Representative failure cases from kimi-k2.6. 12 [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Additional near-miss predictions by our agent. Representative near-miss cases from kimi-k2.6. 13 [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Additional mixed predictions by our agent. Representative cases where the agent predicts the correct answer in some, but not all, independent runs from kimi-k2.6. 14 [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

67 extracted references · 2 linked inside Pith

  1. [1]

    and Kurimoto, T

    Hayamizu, Kikuko and Asakura, K. and Kurimoto, T. , title =. 2015 , howpublished =

    2015

  2. [2]

    arXiv preprint arXiv:2512.01274 , year=

    Superchem: A multimodal reasoning benchmark in chemistry , author=. arXiv preprint arXiv:2512.01274 , year=

    arXiv

  3. [3]

    Wishart and AnChi Guo and Eponine Oler and Fei Wang and Afia Anjum and Harrison Peters and Raynard Dizon and Zinat Sayeeda and Siyang Tian and Brian L

    David S. Wishart and AnChi Guo and Eponine Oler and Fei Wang and Afia Anjum and Harrison Peters and Raynard Dizon and Zinat Sayeeda and Siyang Tian and Brian L. Lee and others , title =. Nucleic Acids Research , volume =. 2022 , doi =

    2022

  4. [4]

    Communications Chemistry , volume =

    Li, Yunrui and Xu, Hao and Kumar, Ambrish and Wang, Duo-Sheng and Heiss, Christian and Azadi, Parastoo and Hong, Pengyu , title =. Communications Chemistry , volume =

  5. [5]

    OpenReview , year =

    2DNMRGym: An Annotated Experimental Dataset for Atom-Level Molecular Representation Learning in 2D NMR via Surrogate Supervision , author =. OpenReview , year =

  6. [6]

    arXiv preprint arXiv:2512.23880 , year =

    CASCADE: Cumulative Agentic Skill Creation through Autonomous Development and Evolution , author =. arXiv preprint arXiv:2512.23880 , year =

    arXiv

  7. [7]

    arXiv preprint arXiv:2505.21318 , year=

    Beyond Chemical QA: Evaluating LLM's Chemical Reasoning with Modular Chemical Operations , author=. arXiv preprint arXiv:2505.21318 , year=

    arXiv

  8. [8]

    Nature , volume=

    A benchmark of expert-level academic questions to assess AI capabilities , author=. Nature , volume=. 2026 , publisher=

    2026

  9. [9]

    Wang, J. J. and Jin, Y. and Zhi, C. Y. and others , title =. Scientific Data , year =

  10. [10]

    Nature Chemistry , volume=

    A framework for evaluating the chemical knowledge and reasoning abilities of large language models against the expertise of chemists , author=. Nature Chemistry , volume=

  11. [11]

    2024 , journal =

    Are large language models superhuman chemists? , author =. 2024 , journal =

    2024

  12. [12]

    2024 , journal =

    Probing the limitations of multimodal language models for chemistry and materials research , author =. 2024 , journal =

    2024

  13. [13]

    arXiv preprint arXiv:2601.15279 , year=

    MolecularIQ: Characterizing Chemical Reasoning Capabilities Through Symbolic Verification on Molecular Graphs , author=. arXiv preprint arXiv:2601.15279 , year=

    arXiv

  14. [14]

    arXiv preprint arXiv:2512.15567 , year=

    Evaluating large language models in scientific discovery , author=. arXiv preprint arXiv:2512.15567 , year=

    Pith/arXiv arXiv

  15. [15]

    NeurIPS 2024 Datasets and Benchmarks Track , year =

    Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistry , author =. NeurIPS 2024 Datasets and Benchmarks Track , year =

    2024

  16. [16]

    arXiv preprint arXiv:2602.10158 , year =

    NMRSpec: Structure Elucidation from Experimental NMR Spectra via Spectrum Language Modeling and Retrieval-Augmented Generation , author =. arXiv preprint arXiv:2602.10158 , year =

    arXiv

  17. [17]

    and White, Andrew D

    Ramos, Mayk Caldas and Collison, Christopher J. and White, Andrew D. , title =. Chemical Science , year =

  18. [18]

    Bragg, Jonathan and D'Arcy, Mike and Balepur, Nishant and Bareket, Dan and Dalvi Mishra, Bhavana and Feldman, Sergey and Haddad, Dany and Hwang, Jena D. and Jansen, Peter and Kishore, Varsha and Majumder, Bodhisattwa Prasad and Naik, Aakanksha and Rahamimov, Sigal and Richardson, Kyle and Singh, Amanpreet and Surana, Harshit and Tiktinsky, Aryeh and Vasu,...

  19. [19]

    2026 , howpublished =

    Laurent, Jon , title =. 2026 , howpublished =

    2026

  20. [20]

    arXiv preprint arXiv:2603.23101 , year=

    SpecXMaster: Agentic Reinforcement Learning for NMR Molecular Spectral Interpretation , author=. arXiv preprint arXiv:2603.23101 , year=

    Pith/arXiv arXiv

  21. [21]

    2025 , note=

    Atomic Diffusion Models for Small Molecule Structure Elucidation from NMR Spectra , author=. 2025 , note=

    2025

  22. [22]

    2026 , note=

    MolQuest: A Benchmark for Agentic Evaluation of Abductive Reasoning in Chemical Structure Elucidation , author=. 2026 , note=

    2026

  23. [23]

    Digital Discovery , year =

    Enhancing molecular structure elucidation with reasoning-capable LLMs , author =. Digital Discovery , year =

  24. [24]

    2025 , note =

    BixBench: a Comprehensive Benchmark for LLM-based Agents in Computational Biology , author=. 2025 , note =

    2025

  25. [25]

    Learning the

    Alberts, Marvin and Zipoli, Federico and Vaucher, Alain , year =. Learning the

  26. [26]

    Angewandte Chemie International Edition , year =

    Advancing Structure Elucidation with a Flexible Multi-Spectral AI Model , author =. Angewandte Chemie International Edition , year =

  27. [27]

    2025 , note =

    DiSE: A Diffusion Probabilistic Model for Automatic Structure Elucidation of Organic Compounds , author =. 2025 , note =

    2025

  28. [28]

    2025 , note =

    IR-Agent: Expert-Inspired LLM Agents for Structure Elucidation from Infrared Spectra , author=. 2025 , note =

    2025

  29. [29]

    Deva , journal =

    Devata, Sriram and Sridharan, Bhuvanesh and Mehta, Sarvesh and Pathak, Yashaswi and Laghuvarapu, Siddhartha and Varma, Girish and Priyakumar, U. Deva , journal =. DeepSPInN -- deep reinforcement learning for molecular structure prediction from infrared and ^. 2024 , volume =

    2024

  30. [30]

    DiffNMR: Diffusion Models for Nuclear Magnetic Resonance Spectra Elucidation , author =

  31. [31]

    2025 , howpublished =

    LABBench2: A Benchmark for Evaluating AI Systems on Practical Biology Research Tasks , author =. 2025 , howpublished =

    2025

  32. [32]

    Analytical Chemistry , year =

    NMRMind: A Transformer-Based Model Enabling Structure Elucidation from Multidimensional NMR , author =. Analytical Chemistry , year =

  33. [33]

    NMIRacle: Multi-Modal Generative Molecular Elucidation , author =

  34. [34]

    Digital Discovery , year =

    DeepSPInN: Deep Spectral Inference Neural Network for Molecular Structure Elucidation , author =. Digital Discovery , year =

  35. [35]

    Chemical Science , year =

    DP4-AI automated NMR data analysis: straight from spectrometer to structure , author =. Chemical Science , year =

  36. [36]

    Journal of Cheminformatics , year =

    DeepSAT: Learning Molecular Structures from Nuclear Magnetic Resonance Data , author =. Journal of Cheminformatics , year =

  37. [37]

    arXiv preprint arXiv:2602.08752 , year =

    Synergistic cross-modal learning for experimental NMR-based structure elucidation , author =. arXiv preprint arXiv:2602.08752 , year =

    arXiv

  38. [38]

    ACS Central Science , year =

    Accurate and Efficient Structure Elucidation from Routine One-Dimensional NMR Spectra Using Multitask Machine Learning , author =. ACS Central Science , year =

  39. [39]

    Chemical Science , volume=

    A machine learning protocol for revealing ion transport mechanisms from dynamic NMR shifts in paramagnetic battery materials , author=. Chemical Science , volume=

  40. [40]

    Machine Learning: Science and Technology , volume=

    Revving up 13C NMR shielding predictions across chemical space: benchmarks for atoms-in-molecules kernel machine learning with new data for 134 kilo molecules , author=. Machine Learning: Science and Technology , volume=

  41. [41]

    Nature Computational Science , year =

    Fanjie Xu and Wentao Guo and Feng Wang and Lin Yao and Hongshuai Wang and Fujie Tang and Zhifeng Gao and Linfeng Zhang and Weinan E and Zhong-Qun Tian and Jun Cheng , title =. Nature Computational Science , year =

  42. [42]

    NMRGym: A Comprehensive Benchmark for Nuclear Magnetic Resonance Based Molecular Structure Elucidation , author=

  43. [43]

    Benji and Jonsson, Lina and Goodman, Jonathan M

    Rowlands, J. Benji and Jonsson, Lina and Goodman, Jonathan M. and Howe, Peter W. A. and Czechtizky, Werngard and Leek, Tomas and Lewis, Richard J. Towards automatically verifying chemical structures: the powerful combination of 1H NMR and IR spectroscopy. Chem. Sci. 2025

    2025

  44. [44]

    McGraw-Hill Companies , year=

    Applications of artificial intelligence for organic chemistry: the DENDRAL project , author=. McGraw-Hill Companies , year=

  45. [45]

    and Bastin, Loyd D

    Van Bramer, Scott E. and Bastin, Loyd D. , title =. Journal of Chemical Education , volume =

  46. [46]

    Nature Communications , volume =

    NMR-Solver: Automated Structure Elucidation via Large-Scale Spectral Matching and Physics-Guided Fragment Optimization , author =. Nature Communications , volume =

  47. [47]

    The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track , year=

    Can LLMs Solve Molecule Puzzles? A Multimodal Benchmark for Molecular Structure Elucidation , author=. The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track , year=

  48. [48]

    Nucleic Acids Research , volume =

    Wishart, David S and Sajed, Tanvir and Pin, Matthew and Poynton, Ella F and Goel, Bharat and Lee, Brian L and Guo, An Chi and Saha, Sukanta and Sayeeda, Zinat and Han, Scott and Berjanskii, Mark and Peters, Harrison and Oler, Eponine and Gautam, Vasuk and Jordan, Tamara and Kim, Jonghyeok and Ledingham, Benjamin and Tretter, Zachary M and Koller, James T ...

    2024

  49. [49]

    Communications Chemistry , author =

    Leveraging infrared spectroscopy for automated structure elucidation , volume =. Communications Chemistry , author =. 2024 , pages =

    2024

  50. [50]

    HMDB 5.0: The Human Metabolome Database for 2022

    Wishart, David S and Guo, Anchi and Oler, Eponine and Wang, Fei and Anjum, Afia and Peters, Harrison and Dizon, Raynard and Sayeeda, Zinat and Tian, Siyang and Lee, Brian L and Berjanskii, Mark and Mah, Robert and Yamamoto, Mai and Jovel, Juan and Torres-Calzada, Claudia and Hiebert-Giesbrecht, Mickel and Lui, Vicki W and Varshavi, Dorna and Varshavi, Dor...

    2022

  51. [51]

    Automated

    Alberts, Marvin and Hartrampf, Nina and Laino, Teodoro , year =. Automated

  52. [52]

    Journal of Biomolecular NMR , author =

    Nmrglue: an open source. Journal of Biomolecular NMR , author =. 2013 , pages =

    2013

  53. [53]

    2002 , author =

    An efficient algorithm for automatic phase correction of NMR spectra based on entropy minimization , journal =. 2002 , author =

    2002

  54. [54]

    and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and

    Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and. Nature Methods , year =

  55. [55]

    Digital Discovery , year =

    Alberts, Marvin and Zipoli, Federico and Laino, Teodoro , title =. Digital Discovery , year =

  56. [56]

    Deep imitation learning for molecular inverse problems , volume =

    Jonas, Eric , year =. Deep imitation learning for molecular inverse problems , volume =. Advances in

  57. [57]

    2022 , pages =

    Nature Methods , author =. 2022 , pages =

    2022

  58. [58]

    , year =

    Bohde, Montgomery and Manjrekar, Mrunali and Wang, Runzhong and Ji, Shuiwang and Coley, Connor W. , year =

  59. [59]

    The Journal of Physical Chemistry A , author =

    Transformer-. The Journal of Physical Chemistry A , author =. 2025 , pages =

    2025

  60. [60]

    2001 , pages =

    Journal of Chemical Information and Computer Sciences , author =. 2001 , pages =

    2001

  61. [61]

    Journal of Chemical Information and Computer Sciences , author =

    Recent. Journal of Chemical Information and Computer Sciences , author =. 1996 , pages =

    1996

  62. [62]

    Journal of Chemical Information and Computer Sciences , author =

    Computer-. Journal of Chemical Information and Computer Sciences , author =. 1998 , pages =

    1998

  63. [63]

    , year =

    Lederberg, J. , year =. How. Proceedings of

  64. [64]

    Structure Elucidation by NMR in Organic Chemistry: A Practical Guide , publisher =

    Breitmayer, Eberhard , year =. Structure Elucidation by NMR in Organic Chemistry: A Practical Guide , publisher =

  65. [65]

    2014 , author =

    Recent advances and new strategies in the NMR-based identification of natural products , journal =. 2014 , author =

    2014

  66. [66]

    and Faulkner, Stephen and Nilsson, Mathias and Morris, Gareth A

    Aguilar, Juan A. and Faulkner, Stephen and Nilsson, Mathias and Morris, Gareth A. , title =. Angewandte Chemie International Edition , volume =

  67. [67]

    1988 , pages =

    Journal of Chemical Information and Computer Sciences , author =. 1988 , pages =

    1988