Vib2Mol: from vibrational spectra to molecular structures-a versatile deep learning model

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 versatile deep learning model designed to flexibly handle diverse spectrum-to-structure tasks according to the available prior knowledge by bridging the retrieval and generation. It not only achieves state-of-the-art performance in analyzing theoretical Infrared and Raman spectra, but also outperform previous models at experimental data. Moreover, Vib2Mol demonstrates promising capabilities in predicting reaction products and sequencing peptides, enabling vibrational spectroscopy a real-time guide for autonomous scientific discovery workflows.