End-to-end molecular structure elucidation from multimodal NMR spectra images using vision transformers

Q: What does this research mean for the field?

An end-to-end vision transformer (NMRViT) can directly predict molecular structures from raw multimodal NMR spectra images, including 1H, 13C, and HSQC. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Chao Han; Xiaolin Pan; Yingkai Zhang

doi:10.1039/d6sc02352e

January 1, 2026Chemical ScienceOpen Access

End-to-end molecular structure elucidation from multimodal NMR spectra images using vision transformers

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Key Points

The aim is to automate molecular structure elucidation by utilizing vision transformers to analyze NMR spectra.
Developed NMRViT, an end-to-end vision transformer model.
Learned directly from raw 1H, 13C, and HSQC NMR spectra images for structure prediction.
Employed multimodal spectral data to enhance predictive accuracy.
Successfully predicted various molecular structures using the NMRViT model.
Demonstrated improved automation in interpreting spectral data for molecular analysis.
Showed potential reduction in the time and effort required for manual interpretation.

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Implication

Study models molecular structures from multimodal NMR spectra, suggesting advancements in automation.

Abstract

NMRViT is an end-to-end vision transformer that learns directly from raw 1 H, 13 C and HSQC NMR spectra to predict molecular structures, advancing automated structure elucidation from multimodal spectral data.

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Cite This Study

Han et al. (2026) studied this question.

synapsesocial.com/papers/6a17db6f3fad632b0f9d8254 https://doi.org/10.1039/d6sc02352e

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End-to-end molecular structure elucidation from multimodal NMR spectra images using vision transformers

Key Points

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Implication

Abstract

Discussion

Cite This Study

End-to-end molecular structure elucidation from multimodal NMR spectra images using vision transformers

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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