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Vib2Conf, a novel deep learning model, directly discriminates 3D molecular conformations from vibrational spectra by addressing spectral ambiguities caused by conformational heterogeneity. It uses an attentional resampler to extract conformation-sensitive features and a Mixture-of-Experts (MoE) to partition the conformational space. Vib2Conf achieves state-of-the-art top-1 recall, exceeding 95% on spectrum-structure benchmarks and 82.06% on discriminating near-isomeric conformers.
Vib2Conf achieves unprecedented accuracy in identifying 3D molecular conformations from vibrational spectra, even distinguishing between near-isomeric conformers differing by only ~1 脜 RMSD.
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 {\AA}. In general, Vib2Conf is a promising method for fine-grained spectrum-to-conformation analysis.
