Neural Spectral Prediction for Structure Elucidation with Tandem Mass Spectrometry.

ABSTRACT: 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 candidate

SUBMITTER: Wang R 

PROVIDER: S-EPMC12154671 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature