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When confronted with a substance of unknown identity, researchers oftenperform mass spectrometry on the sample and compare the observed spectrum to alibrary of previously-collected spectra to identify the molecule. Whilepopular, this approach will fail to identify molecules that are not in theexisting library. In response, we propose to improve the library's coverage byaugmenting it with synthetic spectra that are predicted using machine learning.We contribute a lightweight neural network model that quickly predicts massspectra for small molecules. Achieving high accuracy predictions requires anovel neural network architecture that is designed to capture typicalfragmentation patterns from electron ionization. We analyze the effects of ourmodeling innovations on library matching performance and compare our models toprior machine learning-based work on spectrum prediction.

Rapid Prediction of Electron-Ionization Mass Spectrometry using Neural Networks