JESTR: Joint Embedding Space Technique for Ranking candidate molecules for the annotation of untargeted metabolomics data.

ABSTRACT:

Motivation

A major challenge in metabolomics is annotation: assigning molecular structures to mass spectral fragmentation patterns. Despite recent advances in molecule-to-spectra and in spectra-to-molecular fingerprint (FP) prediction, annotation rates remain low.

Results

We introduce in this article a novel tool (JESTR) for annotation. Unlike prior approaches that "explicitly" construct molecular FPs or spectra, JESTR leverages the insight that molecules and their corresponding spectra are views of the same data and effectively embeds their representations in a joint space. Candidate structures are ranked based on cosine similarity between the embeddings of query spectrum and each candidate. We evaluate JESTR against mol-to-spec, spec-to-FP, and spec-mol matching annotation

SUBMITTER: Kalia A 

PROVIDER: S-EPMC12233093 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature