MIST-CF: Chemical Formula Inference from Tandem Mass Spectra.
Samuel GoldmanJiayi XinJoules ProvenzanoConnor W ColeyPublished in: Journal of chemical information and modeling (2023)
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.
Keyphrases
- ms ms
- neural network
- human milk
- tandem mass spectrometry
- cystic fibrosis
- ultra high performance liquid chromatography
- electronic health record
- high performance liquid chromatography
- mass spectrometry
- multiple sclerosis
- liquid chromatography tandem mass spectrometry
- big data
- simultaneous determination
- liquid chromatography
- emergency department
- high resolution
- data analysis
- low birth weight
- machine learning
- preterm infants
- deep learning
- adverse drug
- artificial intelligence