Learning interpretable structural similarity from tandem mass spectra for small molecule analog discovery

Analog discovery remains a central bottleneck in mass spectrometry-based untargeted metabolomics, as conventional spectral similarity scores poorly reflect molecular structure. We introduce SIMBA, a transformer-based model that infers two interpretable graph-based distances, maximum common edge subgraph and substructure edit distance, directly from tandem mass spectra. SIMBA consistently retrieves structurally closer analogs than existing methods, enabling structure-aware small molecule identification beyond exact spectral matching.

Piedrahita Giraldo, J. S., Da Silva, K. M., Zare Shahneh, M. R., Wang, M., Laukens, K., De Vijlder, T., Bittremieux, W.

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