Pushing the limits of one-dimensional NMR spectroscopy for automated structure elucidation using artificial intelligence
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917160336490496 |
|---|---|
| author | Hu, Frank Tubb, Jonathan M. Argyropoulos, Dimitris Golotvin, Sergey Elyashberg, Mikhail Rotskoff, Grant M. Kanan, Matthew W. Markland, Thomas E. |
| author_facet | Hu, Frank Tubb, Jonathan M. Argyropoulos, Dimitris Golotvin, Sergey Elyashberg, Mikhail Rotskoff, Grant M. Kanan, Matthew W. Markland, Thomas E. |
| contents | One-dimensional NMR spectroscopy is one of the most widely used techniques for the characterization of organic compounds and natural products. For molecules with up to 36 non-hydrogen atoms, the number of possible structures has been estimated to range from $10^{20} - 10^{60}$. The task of determining the structure (formula and connectivity) of a molecule of this size using only its one-dimensional $^1$H and/or $^{13}$C NMR spectrum, i.e. de novo structure generation, thus appears completely intractable. Here we show how it is possible to achieve this task for systems with up to 40 non-hydrogen atoms across the full elemental coverage typically encountered in organic chemistry (C, N, O, H, P, S, Si, B, and the halogens) using a deep learning framework, thus covering a vast portion of the drug-like chemical space. Leveraging insights from natural language processing, we show that our transformer-based architecture predicts the correct molecule with 55.2% accuracy within the first 15 predictions using only the $^1$H and $^{13}$C NMR spectra, thus overcoming the combinatorial growth of the chemical space while also being extensible to experimental data via fine-tuning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_18531 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Pushing the limits of one-dimensional NMR spectroscopy for automated structure elucidation using artificial intelligence Hu, Frank Tubb, Jonathan M. Argyropoulos, Dimitris Golotvin, Sergey Elyashberg, Mikhail Rotskoff, Grant M. Kanan, Matthew W. Markland, Thomas E. Chemical Physics Machine Learning One-dimensional NMR spectroscopy is one of the most widely used techniques for the characterization of organic compounds and natural products. For molecules with up to 36 non-hydrogen atoms, the number of possible structures has been estimated to range from $10^{20} - 10^{60}$. The task of determining the structure (formula and connectivity) of a molecule of this size using only its one-dimensional $^1$H and/or $^{13}$C NMR spectrum, i.e. de novo structure generation, thus appears completely intractable. Here we show how it is possible to achieve this task for systems with up to 40 non-hydrogen atoms across the full elemental coverage typically encountered in organic chemistry (C, N, O, H, P, S, Si, B, and the halogens) using a deep learning framework, thus covering a vast portion of the drug-like chemical space. Leveraging insights from natural language processing, we show that our transformer-based architecture predicts the correct molecule with 55.2% accuracy within the first 15 predictions using only the $^1$H and $^{13}$C NMR spectra, thus overcoming the combinatorial growth of the chemical space while also being extensible to experimental data via fine-tuning. |
| title | Pushing the limits of one-dimensional NMR spectroscopy for automated structure elucidation using artificial intelligence |
| topic | Chemical Physics Machine Learning |
| url | https://arxiv.org/abs/2512.18531 |