DiffNMR: Diffusion Models for Nuclear Magnetic Resonance Spectra Elucidation
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_ | 1866913938836291584 |
|---|---|
| author | Yang, Qingsong Wu, Binglan Liu, Xuwei Chen, Bo Li, Wei Long, Gen Chen, Xin Xiao, Mingjun |
| author_facet | Yang, Qingsong Wu, Binglan Liu, Xuwei Chen, Bo Li, Wei Long, Gen Chen, Xin Xiao, Mingjun |
| contents | Nuclear Magnetic Resonance (NMR) spectroscopy is a central characterization method for molecular structure elucidation, yet interpreting NMR spectra to deduce molecular structures remains challenging due to the complexity of spectral data and the vastness of the chemical space. In this work, we introduce DiffNMR, a novel end-to-end framework that leverages a conditional discrete diffusion model for de novo molecular structure elucidation from NMR spectra. DiffNMR refines molecular graphs iteratively through a diffusion-based generative process, ensuring global consistency and mitigating error accumulation inherent in autoregressive methods. The framework integrates a two-stage pretraining strategy that aligns spectral and molecular representations via diffusion autoencoder (Diff-AE) and contrastive learning, the incorporation of retrieval initialization and similarity filtering during inference, and a specialized NMR encoder with radial basis function (RBF) encoding for chemical shifts, preserving continuity and chemical correlation. Experimental results demonstrate that DiffNMR achieves competitive performance for NMR-based structure elucidation, offering an efficient and robust solution for automated molecular analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08854 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DiffNMR: Diffusion Models for Nuclear Magnetic Resonance Spectra Elucidation Yang, Qingsong Wu, Binglan Liu, Xuwei Chen, Bo Li, Wei Long, Gen Chen, Xin Xiao, Mingjun Chemical Physics Machine Learning Nuclear Magnetic Resonance (NMR) spectroscopy is a central characterization method for molecular structure elucidation, yet interpreting NMR spectra to deduce molecular structures remains challenging due to the complexity of spectral data and the vastness of the chemical space. In this work, we introduce DiffNMR, a novel end-to-end framework that leverages a conditional discrete diffusion model for de novo molecular structure elucidation from NMR spectra. DiffNMR refines molecular graphs iteratively through a diffusion-based generative process, ensuring global consistency and mitigating error accumulation inherent in autoregressive methods. The framework integrates a two-stage pretraining strategy that aligns spectral and molecular representations via diffusion autoencoder (Diff-AE) and contrastive learning, the incorporation of retrieval initialization and similarity filtering during inference, and a specialized NMR encoder with radial basis function (RBF) encoding for chemical shifts, preserving continuity and chemical correlation. Experimental results demonstrate that DiffNMR achieves competitive performance for NMR-based structure elucidation, offering an efficient and robust solution for automated molecular analysis. |
| title | DiffNMR: Diffusion Models for Nuclear Magnetic Resonance Spectra Elucidation |
| topic | Chemical Physics Machine Learning |
| url | https://arxiv.org/abs/2507.08854 |