English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Tandem mass spectra capture fragmentation patterns that provide keystructural information about a molecule. Although mass spectrometry is appliedin many areas, the vast majority of small molecules lack experimental referencespectra. For over seventy years, spectrum prediction has remained a keychallenge in the field. Existing deep learning methods do not leverage globalstructure in the molecule, potentially resulting in difficulties whengeneralizing to new data. In this work we propose a new model, MassFormer, foraccurately predicting tandem mass spectra. MassFormer uses a graph transformerarchitecture to model long-distance relationships between atoms in themolecule. The transformer module is initialized with parameters obtainedthrough a chemical pre-training task, then fine-tuned on spectral data.MassFormer outperforms competing approaches for spectrum prediction on multipledatasets, and is able to recover prior knowledge about the effect of collisionenergy on the spectrum. By employing gradient-based attribution methods, wedemonstrate that the model can identify relationships between fragment peaks.To further highlight MassFormer's utility, we show that it can match or exceedexisting prediction-based methods on two spectrum identification tasks. Weprovide open-source implementations of our model and baseline approaches, withthe goal of encouraging future research in this area.

MassFormer: Tandem Mass Spectrum Prediction for Small Molecules using Graph Transformers