Untargeted tandem mass spectrometry (MS/MS) detects thousands of small molecules per biological sample, yet most go unidentified because they are absent from spectral libraries. These uncharacterized metabolites and natural products are precisely the compounds that matter for drug discovery, biomarker research, and exposomics. Computational de novo structure elucidation could close this gap, but almost all state-of-the-art methods assume the ground-truth molecular formula is known, an oracle that does not exist for genuinely novel compounds, whose formula must itself be predicted with substantial error. We present MARLIN, a de novo method that elucidates structures directly from a spectrum with no molecular formula at any stage. A self-supervised encoder predicts a molecular fingerprint from the raw peaks, and a block-diffusion language model generates candidate structures conditioned only on the fingerprint and the instrument-measured precursor mass. A mass-shell constraint, whose heavy-atom prune provably never excludes the true molecule, steers generation toward the measured mass without fixing the atom inventory, and candidates are accepted by exact parts-per-million mass agreement. A symmetric noise objective absorbs encoder error, and a candidate-diversity mechanism keeps the candidates from collapsing to a single structure. On the NPLIB1 benchmark, MARLIN reaches 16.9% Top-1 exact-match accuracy, higher than the formula-given baselines, and returns the closest candidate structures by structural distance and fingerprint similarity. It recovers the correct molecular formula as a byproduct about as often as a dedicated predictor without ever using one. MARLIN brings de novo structure elucidation to the discovery regime where the molecular formula is unavailable.

翻译：暂无翻译