MassFormer: Tandem Mass Spectrum Prediction with Graph Transformers.
Year: 2021
Type: preprint
Abstract: Mass spectrometry is a key tool in the study of small molecules, playing animportant role in metabolomics, drug discovery, and environmental chemistry.
Tandem mass spectra capture fragmentation patterns that provide key structural
information about a molecule and help with its identification. Practitioners
often rely on spectral library searches to match unknown spectra with known
compounds. However, such search-based methods are limited by availability of
reference experimental data. In this work we show that graph transformers can
be used to accurately predict tandem mass spectra. Our model, MassFormer,
outperforms competing deep learning approaches for spectrum prediction, and
includes an interpretable attention mechanism to help explain predictions. We
demonstrate that our model can be used to improve reference library coverage on
a synthetic molecule identification task. Through quantitative analysis and
visual inspection, we verify that our model recovers prior knowledge about the
effect of collision energy on the generated spectrum. We evaluate our model on
different types of mass spectra from two independent MS datasets and show that
its performance generalizes. Code available at github.com/Roestlab/massformer.
Source: arXiv (Cornell University)
Authors: Adamo Young, Bo Wang, Hannes Röst
Language: English
Subfield: Spectroscopy
Field: Chemistry
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3213127212