Vib2Conf: AI-Driven Discrimination of Molecular Conformations from Vibrational Spectra
Xin-Yu Lu1,2,3, De-Yi Lin3,4, Hao Ma1,3,4
1State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen361005, China.
Abstract:
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.
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