biostudies-literatureUnknownWang RNIA NIH HHSNIAID NIH HHSNCI NIH HHSWellcome TrustNIGMS NIH HHSStructural elucidation using untargeted tandem mass spectrometry (MS/MS) has played a critical role in advancing scientific discovery [1, 2]. However, differentiating molecular fragmentation patterns between isobaric structures remains a prominent challenge in metabolomics [3-10], drug discovery [11-13], and reaction screening [14-17], presenting a significant barrier to the cost-effective and rapid identification of unknown molecular structures. Here, we present a geometric deep learning model, ICEBERG, that simulates collision-induced dissociation in mass spectrometry to generate chemically plausible fragments and their relative intensities with awareness of collision energies and polarities. We utilize ICEBERG predictions to facilitate structure elucidation by ranking a set of candidatebioRxiv : the preprint server for biology2025.05.28.656653https://www.ebi.ac.uk/biostudies/studies/S-EPMC12154671biostudies-literatureNeural Spectral Prediction for Structure Elucidation with Tandem Mass Spectrometry.PMC12154671T32 GM087237110179/Z/15/Z206724/Z/17/ZU01 CA176726R01 AI145781R01 AG051600R01 CA067262Plata DLAvila-Pacheco JClish CBMashin ELederbauer MWeng JKWang MColey CWWang RManjrekar MReynolds EProvenzano JMahjour BGoldman SfalseNeural Spectral Prediction for Structure Elucidation with Tandem Mass Spectrometry.Structural elucidation using untargeted tandem mass spectrometry (MS/MS) has played a critical role in advancing scientific discovery [1, 2]. However, differentiating molecular fragmentation patterns between isobaric structures remains a prominent challenge in metabolomics [3-10], drug discovery [11-13], and reaction screening [14-17], presenting a significant barrier to the cost-effective and rapid identification of unknown molecular structures. Here, we present a geometric deep learning model, ICEBERG, that simulates collision-induced dissociation in mass spectrometry to generate chemically plausible fragments and their relative intensities with awareness of collision energies and polarities. We utilize ICEBERG predictions to facilitate structure elucidation by ranking a set of candidate2025-01-01T00:00:00Z2025 Jun2026-06-15T03:16:21.934Z2026-06-15T03:08:58.531ZS-EPMC121546714050172510.1101/2025.05.28.656653