DiffSpectra: Molecular Structure Elucidation from Spectra using
  Diffusion Models
Molecular structure elucidation from spectra is a foundational problem in
chemistry, with profound implications for compound identification, synthesis,
and drug development. Traditional methods rely heavily on expert interpretation
and lack scalability. Pioneering machine learning methods have introduced
retrieval-based strategies, but their reliance on finite libraries limits
generalization to novel molecules. Generative models offer a promising
alternative, yet most adopt autoregressive SMILES-based architectures that
overlook 3D geometry and struggle to integrate diverse spectral modalities. In
this work, we present DiffSpectra, a generative framework that directly infers
both 2D and 3D molecular structures from multi-modal spectral data using
diffusion models. DiffSpectra formulates structure elucidation as a conditional
generation process. Its denoising network is parameterized by Diffusion
Molecule Transformer, an SE(3)-equivariant architecture that integrates
topological and geometric information. Conditioning is provided by SpecFormer,
a transformer-based spectral encoder that captures intra- and inter-spectral
dependencies from multi-modal spectra. Extensive experiments demonstrate that
DiffSpectra achieves high accuracy in structure elucidation, recovering exact
structures with 16.01% top-1 accuracy and 96.86% top-20 accuracy through
sampling. The model benefits significantly from 3D geometric modeling,
SpecFormer pre-training, and multi-modal conditioning. These results highlight
the effectiveness of spectrum-conditioned diffusion modeling in addressing the
challenge of molecular structure elucidation. To our knowledge, DiffSpectra is
the first framework to unify multi-modal spectral reasoning and joint 2D/3D
generative modeling for de novo molecular structure elucidation.
686f4142d938c25d68441b98
Liang Wang
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2025-07-10T07:09:02.989Z
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Liang Wang
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AzureLeon1
686f4142d938c25d68441b99
Yu Rong
claimed_verified
2025-07-10T13:14:04.210Z
642eecbf9b2484d7d8526781
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Yu Rong
user
Swrooy
686f4142d938c25d68441b9a
Tingyang Xu
686f4142d938c25d68441b9b
Zhenyi Zhong
686f4142d938c25d68441b9c
Zhiyuan Liu
686f4142d938c25d68441b9d
Pengju Wang
686f4142d938c25d68441b9e
Deli Zhao
686f4142d938c25d68441b9f
Qiang Liu
686f4142d938c25d68441ba0
Shu Wu
686f4142d938c25d68441ba1
Liang Wang
DiffSpectra: Molecular Structure Elucidation from Spectra using
  Diffusion Models
Molecular structure elucidation from spectra is a foundational problem in
chemistry, with profound implications for compound identification, synthesis,
and drug development. Traditional methods rely heavily on expert interpretation
and lack scalability. Pioneering machine learning methods have introduced
retrieval-based strategies, but their reliance on finite libraries limits
generalization to novel molecules. Generative models offer a promising
alternative, yet most adopt autoregressive SMILES-based architectures that
overlook 3D geometry and struggle to integrate diverse spectral modalities. In
this work, we present DiffSpectra, a generative framework that directly infers
both 2D and 3D molecular structures from multi-modal spectral data using
diffusion models. DiffSpectra formulates structure elucidation as a conditional
generation process. Its denoising network is parameterized by Diffusion
Molecule Transformer, an SE(3)-equivariant architecture that integrates
topological and geometric information. Conditioning is provided by SpecFormer,
a transformer-based spectral encoder that captures intra- and inter-spectral
dependencies from multi-modal spectra. Extensive experiments demonstrate that
DiffSpectra achieves high accuracy in structure elucidation, recovering exact
structures with 16.01% top-1 accuracy and 96.86% top-20 accuracy through
sampling. The model benefits significantly from 3D geometric modeling,
SpecFormer pre-training, and multi-modal conditioning. These results highlight
the effectiveness of spectrum-conditioned diffusion modeling in addressing the
challenge of molecular structure elucidation. To our knowledge, DiffSpectra is
the first framework to unify multi-modal spectral reasoning and joint 2D/3D
generative modeling for de novo molecular structure elucidation.
DiffSpectra, a generative framework using diffusion models, infers both 2D and 3D molecular structures from multi-modal spectral data with high accuracy.
diffusion models Diffusion Molecule Transformer SE(3)-equivariant architecture SpecFormer transformer-based spectral encoder multi-modal spectral reasoning joint 2D/3D generative modeling de novo molecular structure elucidation