Hypothesis‐and‐Refinement Learning of Organic Structures From Multimodal Spectroscopic Data

ABSTRACT Determining molecular structures from spectroscopic data remains fundamentally challenging because the inverse problem is intrinsically underdetermined: individual spectra are sparse, low‐dimensional, and encode only partial structural evidence relative to the vast space of possible molecules. We address this challenge by formulating automated structure elucidation as a scalable hypothesis‐refinement paradigm that tightly integrates spectral evidence with large‐scale molecular priors. To supply structure‐resolving NMR signals for multimodal learning, we construct QM9SPIN , a DFT‐derived dataset comprising diverse 1D and 2D spectra, including J‐coupling, DEPT experiments, and explicit spin–spin interactions. On this foundation, we introduce SpectroMol , a spectrum‐to‐structure model that proposes chemically valid molecular hypotheses conditioned on multimodal spectral inputs. Complementarily, we develop MS‐Mol2Mol , a high‐resolution mass‐constrained molecular generator that integrates molecular formula, exact mass, and degree of unsaturation within a conditional generative prior trained on 400 million molecules, ensuring global compositional consistency and chemically realistic refinement. The integrated system achieves 93.8% top‐1 accuracy on the simulated benchmark, adapts effectively from simulated to experimental spectra with limited experimental fine‐tuning, and further improves experimental predictions through mass‐guided refinement, establishing a scalable route toward automated, data‐driven organic structure elucidation.