Generating Molecular Fragmentation Graphs with Autoregressive Neural Networks.

ABSTRACT: The accurate prediction of tandem mass spectra from molecular structures has the potential to unlock new metabolomic discoveries by augmenting the community's libraries of experimental reference standards. Cheminformatic spectrum prediction strategies use a "bond-breaking" framework to iteratively simulate mass spectrum fragmentations, but these methods are (a) slow due to the need to exhaustively and combinatorially break molecules and (b) inaccurate as they often rely upon heuristics to predict the intensity of each resulting fragment; neural network alternatives mitigate computational cost but are black-box and not inherently more accurate. We introduce a physically grounded neural approach that learns to predict each breakage event and score the most relevant subset of molecular fragme

SUBMITTER: Goldman S 

PROVIDER: S-EPMC12573212 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature