Rapid Approximate Subset-Based Spectra Prediction for Electron Ionization-Mass Spectrometry.

ABSTRACT: Mass spectrometry is a vital tool in the analytical chemist's toolkit, commonly used to identify the presence of known compounds and elucidate unknown chemical structures. All of these applications rely on having previously measured spectra for known substances. Computational methods for predicting mass spectra from chemical structures can be used to augment existing spectral databases with predicted spectra from previously unmeasured molecules. In this paper, we present a method for prediction of electron ionization-mass spectra (EI-MS) of small molecules that combines physically plausible substructure enumeration and deep learning, which we term rapid approximate subset-based spectra prediction (RASSP). The first of our two models, FormulaNet, produces a probability distribution o

SUBMITTER: Zhu RL 

PROVIDER: S-EPMC9909676 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature