A Multimodal Representation Learning Framework for Molecular Graph and NMR Spectrum Alignment.

Area of Science:

• Computational chemistry
• Spectroscopy
• Machine learning

Background:

• Accurate matching of molecular structures to NMR spectra is crucial for automated structure elucidation.
• Current methods struggle with multi-scale molecular topology and integrating complementary 1H and 13C NMR data.

Purpose of the Study:

• To develop an advanced multimodal matching framework, SpecMol-MatchNet, for enhanced molecular structure-NMR spectra correlation.
• To overcome limitations in existing methods for modeling molecular topology and spectral data fusion.

Main Methods:

• Implemented a hybrid molecular graph encoder with attention-based graph interaction and multi-scale neighborhood aggregation.
• Developed branch-specific spectral feature learning with attention enhancement and joint gating for paired 1H and 13C NMR data.
• Utilized a residual multimodal fusion module to integrate molecular and spectral representations for matching prediction.

Main Results:

• SpecMol-MatchNet demonstrated superior performance compared to existing baseline methods on benchmark datasets.
• The framework effectively captures multi-scale molecular topology and leverages complementary spectral information.
• Achieved consistently better overall performance in matching molecular structures with NMR spectra.

Conclusions:

• SpecMol-MatchNet offers a significant advancement in automated structure elucidation through improved molecular structure-NMR spectra matching.
• The proposed multimodal approach effectively addresses challenges in integrating complex molecular and spectral data.
• This framework provides a robust solution for accurate chemical structure identification using NMR data.