# Accurate and efficient structure elucidation from routine one-dimensional NMR spectra using multitask machine learning

arXiv: https://arxiv.org/abs/2408.08284 · HTML: https://mlsanity.com/paper/2408.08284/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 49 references that are in the map.
- First posted on arXiv 15 Aug 2024.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [Escaping the Big Data Paradigm with Compact Transformers](https://mlsanity.com/paper/2104.05704/) (2021)
- ["Found in Translation": Predicting Outcomes of Complex Organic Chemistry Reactions using Neural Sequence-to-Sequence Models](https://mlsanity.com/paper/1711.04810/) (2017)

## Built on by (most cited first, 21 of 21)

- [Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach](https://mlsanity.com/paper/2506.03979/) (2025)
- [Assessing the Chemical Intelligence of Large Language Models](https://mlsanity.com/paper/2505.07735/) (2025)
- [NMR-Solver: Automated Structure Elucidation via Large-Scale Spectral Matching and Physics-Guided Fragment Optimization](https://mlsanity.com/paper/2509.00640/) (2025)
- [Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond](https://mlsanity.com/paper/2502.09897/) (2025)
- [DiffSpectra: Molecular Structure Elucidation from Spectra using Diffusion Models](https://mlsanity.com/paper/2507.06853/) (2025)
- [DiffNMR: Diffusion Models for Nuclear Magnetic Resonance Spectra Elucidation](https://mlsanity.com/paper/2507.08854/) (2025)
- [Test-Time Tuned Language Models Enable End-to-end De Novo Molecular Structure Generation from MS/MS Spectra](https://mlsanity.com/paper/2510.23746/) (2025)
- [Atomic Diffusion Models for Small Molecule Structure Elucidation from NMR Spectra](https://mlsanity.com/paper/2512.03127/) (2025)
- [SpectraLLM: Uncovering the Ability of LLMs for Molecular Structure Elucidation from Multi-Spectral Data](https://mlsanity.com/paper/2508.08441/) (2025)
- [SpectrumWorld: Artificial Intelligence Foundation for Spectroscopy](https://mlsanity.com/paper/2508.01188/) (2025)
- [SpecMol: A Spectroscopy-Grounded Foundation Model for Multi-Task Molecular Learning](https://mlsanity.com/paper/2509.21861/) (2025)
- [NMIRacle: Multi-modal Generative Molecular Elucidation from IR and NMR Spectra](https://mlsanity.com/paper/2512.19733/) (2025)
- [NMRTrans: Structure Elucidation from Experimental NMR Spectra via Set Transformers](https://mlsanity.com/paper/2602.10158/) (2026)
- [A large-scale foundation model enables simulation-to-real adaptation for nuclear magnetic resonance-based molecular structure analysis](https://mlsanity.com/paper/2606.20756/) (2026)
- [Cross-Modal Characterization of Thin Film MoS2 Using Generative Models](https://mlsanity.com/paper/2505.24065/) (2025)
- [SpecXMaster Technical Report](https://mlsanity.com/paper/2603.23101/) (2026)
- [Towards Generalizable and Evidential Nuclear Magnetic Resonance-Based Molecular Structure Elucidation via Large Language Model Agent](https://mlsanity.com/paper/2606.29776/) (2026)
- [NMR Elucidation as an Agentic Search Problem, Not a Modeling Problem](https://mlsanity.com/paper/2607.19406/) (2026)
- [Hypothesis-and-Refinement Learning of Organic Structures from Multimodal Spectroscopic Data](https://mlsanity.com/paper/2607.19816/) (2026)
- [Multi-Agent Closed-Loop Reasoning for Organic Structure Elucidation from Multimodal Spectra](https://mlsanity.com/paper/2608.14720/) (2026)
- [S3C-LLM: Skill-Code Guided Agentic Language Models for Spectrum-to-Structure Elucidation](https://mlsanity.com/paper/2608.30910/) (2026)

## Abstract (arXiv)

Rapid determination of molecular structures can greatly accelerate workflows across many chemical disciplines. However, elucidating structure using only one-dimensional (1D) NMR spectra, the most readily accessible data, remains an extremely challenging problem because of the combinatorial explosion of the number of possible molecules as the number of constituent atoms is increased. Here, we introduce a multitask machine learning framework that predicts the molecular structure (formula and connectivity) of an unknown compound solely based on its 1D 1H and/or 13C NMR spectra. First, we show how a transformer architecture can be constructed to efficiently solve the task, traditionally performed by chemists, of assembling large numbers of molecular fragments into molecular structures. Integrating this capability with a convolutional neural network (CNN), we build an end-to-end model for predicting structure from spectra that is fast and accurate. We demonstrate the effectiveness of this framework on molecules with up to 19 heavy (non-hydrogen) atoms, a size for which there are trillions of possible structures. Without relying on any prior chemical knowledge such as the molecular formula, we show that our approach predicts the exact molecule 69.6% of the time within the first 15 predictions, reducing the search space by up to 11 orders of magnitude.