Vib2Conf: AI-driven discrimination of molecular conformations from vibrational spectra
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| author | Lu, Xin-Yu Lin, De-Yi Zhu, Tong Ren, Bin Ma, Hao Liu, Guo-Kun |
| author_facet | Lu, Xin-Yu Lin, De-Yi Zhu, Tong Ren, Bin Ma, Hao Liu, Guo-Kun |
| contents | Retrieving or generating two-dimensional molecular structures on the basis of vibrational spectra has been well demonstrated via deep learning models. However, deciphering three-dimensional molecular conformations is still challenging, primarily due to spectral ambiguities caused by conformational heterogeneity, which are difficult to resolve. To address this limitation, we propose Vib2Conf, a deep learning model directly discriminating 3D molecular conformations from vibrational spectra. We implement an attentional resampler to distill conformation-sensitive features from sparse spectral signals, and integrate Mixture-of-Experts (MoE) to partition the conformational space for precise geometric mapping. These modules enable Vib2Conf to achieve state-of-the-art top-1 recall exceeding 95% on traditional spectrum-structure benchmarks, including QM9S, VB-Mols, and QMe14S. More importantly, Vib2Conf can discriminate near-isomeric conformers with a top-1 recall of 82.06% on VB-Confs test set, where conformational isomers differ by a root-mean-square deviation (RMSD) of only ~1 Å. In general, Vib2Conf is a promising method for fine-grained spectrum-to-conformation analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_24310 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Vib2Conf: AI-driven discrimination of molecular conformations from vibrational spectra Lu, Xin-Yu Lin, De-Yi Zhu, Tong Ren, Bin Ma, Hao Liu, Guo-Kun Chemical Physics Retrieving or generating two-dimensional molecular structures on the basis of vibrational spectra has been well demonstrated via deep learning models. However, deciphering three-dimensional molecular conformations is still challenging, primarily due to spectral ambiguities caused by conformational heterogeneity, which are difficult to resolve. To address this limitation, we propose Vib2Conf, a deep learning model directly discriminating 3D molecular conformations from vibrational spectra. We implement an attentional resampler to distill conformation-sensitive features from sparse spectral signals, and integrate Mixture-of-Experts (MoE) to partition the conformational space for precise geometric mapping. These modules enable Vib2Conf to achieve state-of-the-art top-1 recall exceeding 95% on traditional spectrum-structure benchmarks, including QM9S, VB-Mols, and QMe14S. More importantly, Vib2Conf can discriminate near-isomeric conformers with a top-1 recall of 82.06% on VB-Confs test set, where conformational isomers differ by a root-mean-square deviation (RMSD) of only ~1 Å. In general, Vib2Conf is a promising method for fine-grained spectrum-to-conformation analysis. |
| title | Vib2Conf: AI-driven discrimination of molecular conformations from vibrational spectra |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2604.24310 |