FraGNNet: A Deep Probabilistic Model for Tandem Mass Spectrum Prediction

Adamo Young, Fei Wang, David Wishart, BO WANG, Russell Greiner, Hannes Rost

Abstract: Compound identification from tandem mass spectrometry (MS/MS) data is a critical step in the analysis of complex mixtures. Typical solutions for the MS/MS spectrum to compound (MS2C) problem involve comparing the unknown spectrum against a library of known spectrum-molecule pairs, an approach that is limited by incomplete library coverage. Compound to MS/MS spectrum (C2MS) models can improve retrieval rates by augmenting real libraries with predicted MS/MS spectra. Unfortunately, many existing C2MS models suffer from problems with mass accuracy, generalization, or interpretability. We develop a new probabilistic method for C2MS prediction, FraGNNet, that can efficiently and accurately simulate MS/MS spectra with high mass accuracy. Our approach formulates the C2MS problem as learning a distribution over molecule fragments. FraGNNet achieves state-of-the-art performance in terms of prediction error and surpasses existing C2MS models as a tool for retrieval-based MS2C.

Submission Length: Regular submission (no more than 12 pages of main content)

Changes Since Last Submission:

Camera-ready version
- Addition of new retrieval results in Section 5.2 and Appendix A.11
- Explanation of similarity metrics in Appendix A.6
- Addition of inference speed experiments in Appendix A.19
- Various small text changes

Code: https://github.com/FraGNNet/fragnnet

Assigned Action Editor: ~John_Timothy_Halloran1

Submission Number: 4746