Generating Molecular Fragmentation Graphs with Autoregressive Neural Networks
Name
2304.13136v2.pdf
Description
Accepted version
Size
1.09 MB
Format
Adobe PDF
Checksum (MD5)
60fe1387f10c0f171da399fc465bf18b
Author(s)
Goldman, Samuel
Li, Janet
Coley, Connor W
Date Issued
February 27, 2024
Journal
Analytical Chemistry
Publisher
American Chemical Society
Citation
Samuel Goldman, Janet Li, and Connor W. Coley. Analytical Chemistry 2024 96 (8), 3419-3428.
Version
Author's final manuscript
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 fragments quickly and accurately. We evaluate our model by predicting spectra from both public and private standard libraries, demonstrating that our hybrid approach offers state-of-the-art prediction accuracy, improved metabolite identification from a database of candidates, and higher interpretability when compared to previous breakage methods and black-box neural networks. The grounding of our approach in physical fragmentation events shows especially great promise for elucidating natural product molecules with more complex scaffolds.
MIT Department
Massachusetts Institute of Technology. Computational and Systems Biology Program
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-ShareAlike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1021/acs.analchem.3c04654
