MolSpecFlow: Modality-Incomplete Molecular--Spectral Learning for MS/MS

Untargeted metabolomics produces massive numbers of tandem mass spectra, yet most spectra remain difficult to assign to molecular structures because each spectrum is only a partial, instrument-dependent observation of the underlying molecule. We study MS/MS annotation as modality-incomplete molecular--spectral inference: molecules, spectra, and fingerprints provide complementary views of the same chemical entity, but fully paired molecule--spectrum data are limited and many training examples contain only molecular or only spectral information. We introduce MolSpecFlow, a mask-conditioned heterogeneous flow framework that represents these modalities as coupled components of a shared molecular--spectral state. Availability masks encode which components exist in each example, and observation masks specify which components are conditioned on or generated, instantiating de novo annotation, molecular retrieval, and spectrum simulation within one backbone. MolSpecFlow uses a shared Mol-Spec Transformer, hybrid discrete--continuous flow dynamics, and expected mass/formula regularization over soft molecular token distributions. Experiments show consistent gains from modality-incomplete pretraining, multi-interface reuse, and external de novo transfer.