MIST-CF: Chemical Formula Inference from Tandem Mass Spectra
Name
MIST_CF.pdf
Description
Accepted version
Size
5.88 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Goldman, Samuel
Xin, Jiayi
Provenzano, Joules
Coley, Connor W
Date Issued
September 19, 2023
Journal
Journal of Chemical Information and Modeling
Publisher
American Chemical Society
Citation
Goldman, Samuel, Xin, Jiayi, Provenzano, Joules and Coley, Connor W. 2023. "MIST-CF: Chemical Formula Inference from Tandem Mass Spectra." Journal of Chemical Information and Modeling, 64 (7).
Version
Author's final manuscript
Abstract
Chemical formula annotation for tandem mass spectrometry (MS/MS) data is the first step toward structurally elucidating unknown metabolites. While great strides have been made toward solving this problem, the current state-of-the-art method depends on time-intensive, proprietary, and expert-parametrized fragmentation tree construction and scoring. In this work, we extend our previous spectrum Transformer methodology into an energy-based modeling framework, MIST-CF: Metabolite Inference with Spectrum Transformers for Chemical Formula prediction, for learning to rank chemical formula and adduct assignments given an unannotated MS/MS spectrum. Importantly, MIST-CF learns in a data-dependent fashion using a Formula Transformer neural network architecture and circumvents the need for fragmentation tree construction. We train and evaluate our model on a large open-access database, showing an absolute improvement of 10% top 1 accuracy over other neural network architectures. We further validate our approach on the CASMI2022 challenge data set, achieving nearly equivalent performance to the winning entry within the positive mode category without any manual curation or postprocessing of our results. These results demonstrate an exciting strategy to more powerfully leverage MS2 fragment peaks for predicting MS1 precursor chemical formulas with data-driven learning.
MIT Department
Massachusetts Institute of Technology. Computational and Systems Biology Program
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
10.1021/acs.jcim.3c01082
