Vib2Mol: from vibrational spectra to molecular structures-a unified deep learning framework
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914264244027392 |
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| author | Lu, Xinyu Ma, Hao Li, Hui Li, Jia Rong, Yi Li, Yuqiang Zhu, Tong Liu, Guokun Ren, Bin |
| author_facet | Lu, Xinyu Ma, Hao Li, Hui Li, Jia Rong, Yi Li, Yuqiang Zhu, Tong Liu, Guokun Ren, Bin |
| contents | There will be a paradigm shift in chemical and biological research, to be enabled by autonomous, closed-loop, real-time self-directed decision-making experimentation. Spectrum-to-structure correlation, which is to elucidate molecular structures with spectral information, is the core step in understanding the experimental results and to close the loop. However, current approaches usually divide the task into either database-dependent retrieval and database-independent generation and neglect the inherent complementarity between them. In this study, we proposed Vib2Mol, a unified deep learning framework designed to flexibly handle diverse spectrum-to-structure tasks according to the available prior knowledge by bridging the retrieval and generation. Empowered by our coarse-to-fine retrieval and generate-then-rerank strategies, Vib2Mol not only achieves state-of-the-art performance in analyzing theoretical Infrared and Raman spectra, but also outperform previous models on experimental data. Moreover, our model demonstrates promising capabilities in predicting reaction products and sequencing peptides, enabling vibrational spectroscopy a potential guide for autonomous scientific discovery workflows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07014 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Vib2Mol: from vibrational spectra to molecular structures-a unified deep learning framework Lu, Xinyu Ma, Hao Li, Hui Li, Jia Rong, Yi Li, Yuqiang Zhu, Tong Liu, Guokun Ren, Bin Chemical Physics There will be a paradigm shift in chemical and biological research, to be enabled by autonomous, closed-loop, real-time self-directed decision-making experimentation. Spectrum-to-structure correlation, which is to elucidate molecular structures with spectral information, is the core step in understanding the experimental results and to close the loop. However, current approaches usually divide the task into either database-dependent retrieval and database-independent generation and neglect the inherent complementarity between them. In this study, we proposed Vib2Mol, a unified deep learning framework designed to flexibly handle diverse spectrum-to-structure tasks according to the available prior knowledge by bridging the retrieval and generation. Empowered by our coarse-to-fine retrieval and generate-then-rerank strategies, Vib2Mol not only achieves state-of-the-art performance in analyzing theoretical Infrared and Raman spectra, but also outperform previous models on experimental data. Moreover, our model demonstrates promising capabilities in predicting reaction products and sequencing peptides, enabling vibrational spectroscopy a potential guide for autonomous scientific discovery workflows. |
| title | Vib2Mol: from vibrational spectra to molecular structures-a unified deep learning framework |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2503.07014 |