2023/03/11 by Samuel Goldman, Goldman, Samuel, John L. Bradshaw +5 · 3 citations
Biochemistry, Genetics and Molecular Biology · Chemistry · Computer Science · #Analytical Chemistry and Chromatography #Computational Drug Discovery Methods #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Metabolomics and Mass Spectrometry Studies #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.2303.06470
openalex publication_date 2023/03/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Computational predictions of mass spectra from molecules have enabled the discovery of clinically relevant metabolites. However, such predictive tools are still limited as they occupy one of two extremes, either operating (a) by fragmenting molecules combinatorially with overly rigid constraints on potential rearrangements and poor time complexity or (b) by decoding lossy and nonphysical discretized spectra vectors. In this work, we use a new intermediate strategy for predicting mass spectra from molecules by treating mass spectra as sets of molecular formulae, which are themselves multisets of atoms. After first encoding an input molecular graph, we decode a set of molecular subformulae, each of which specify a predicted peak in the mass spectrum, the intensities of which are predicted by a second model. Our key insight is to overcome the combinatorial possibilities for molecular subformulae by decoding the formula set using a prefix tree structure, atom-type by atom-type, representing a general method for ordered multiset decoding. We show promising empirical results on mass spectra prediction tasks.