Enhancing Peak Assignment in 13C NMR Spectroscopy: A Novel Approach Using Multimodal Alignment
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Acceso en línea: | |
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| _version_ | 1866913446184878080 |
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| author | Xu, Hao Zhou, Zhengyang Hong, Pengyu |
| author_facet | Xu, Hao Zhou, Zhengyang Hong, Pengyu |
| contents | Nuclear magnetic resonance (NMR) spectroscopy plays an essential role in deciphering molecular structure and dynamic behaviors. While AI-enhanced NMR prediction models hold promise, challenges still persist in tasks such as molecular retrieval, isomer recognition, and peak assignment. In response, this paper introduces a novel solution, Multi-Level Multimodal Alignment with Knowledge-Guided Instance-Wise Discrimination (K-M3AID), which establishes correspondences between two heterogeneous modalities: molecular graphs and NMR spectra. K-M3AID employs a dual-coordinated contrastive learning architecture with three key modules: a graph-level alignment module, a node-level alignment module, and a communication channel. Notably, K-M3AID introduces knowledge-guided instance-wise discrimination into contrastive learning within the node-level alignment module. In addition, K-M3AID demonstrates that skills acquired during node-level alignment have a positive impact on graph-level alignment, acknowledging meta-learning as an inherent property. Empirical validation underscores K-M3AID's effectiveness in multiple zero-shot tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_13817 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Enhancing Peak Assignment in 13C NMR Spectroscopy: A Novel Approach Using Multimodal Alignment Xu, Hao Zhou, Zhengyang Hong, Pengyu Machine Learning Chemical Physics Quantitative Methods Nuclear magnetic resonance (NMR) spectroscopy plays an essential role in deciphering molecular structure and dynamic behaviors. While AI-enhanced NMR prediction models hold promise, challenges still persist in tasks such as molecular retrieval, isomer recognition, and peak assignment. In response, this paper introduces a novel solution, Multi-Level Multimodal Alignment with Knowledge-Guided Instance-Wise Discrimination (K-M3AID), which establishes correspondences between two heterogeneous modalities: molecular graphs and NMR spectra. K-M3AID employs a dual-coordinated contrastive learning architecture with three key modules: a graph-level alignment module, a node-level alignment module, and a communication channel. Notably, K-M3AID introduces knowledge-guided instance-wise discrimination into contrastive learning within the node-level alignment module. In addition, K-M3AID demonstrates that skills acquired during node-level alignment have a positive impact on graph-level alignment, acknowledging meta-learning as an inherent property. Empirical validation underscores K-M3AID's effectiveness in multiple zero-shot tasks. |
| title | Enhancing Peak Assignment in 13C NMR Spectroscopy: A Novel Approach Using Multimodal Alignment |
| topic | Machine Learning Chemical Physics Quantitative Methods |
| url | https://arxiv.org/abs/2311.13817 |