# Learning from All Views: A Multiview Contrastive Framework for Metabolite Annotation

## TL;DR

This paper introduces MVP, a new method for improving metabolite annotation by combining multiple data views into a shared learning framework.

## Contribution

MVP introduces a multiview contrastive learning framework that jointly learns from multiple data views to improve metabolite annotation.

## Key findings

- MVP outperforms rank aggregation strategies using consensus spectra for metabolite annotation.
- Using consensus spectra, MVP achieves 36.0% and 14.0% rank@1 for mass and formula-based retrieval.
- MVP performs as well or better than existing methods when using individual spectra.

## Abstract

Metabolomics, enabled by high-throughput mass spectrometry,
promises
to advance our understanding of cellular biochemistry and guide new
discoveries in disease mechanisms, drug development, and personalized
medicine. However, as the assignment of molecular structures to measured
spectra is challenging, annotation rates remain low and hinder potential
advancements. We present MultiView Projection (MVP), a novel framework
for learning a joint embedding space between molecules and spectra
by leveraging multiple data views: molecular graphs, molecular fingerprints,
spectra, and consensus spectra. MVP builds on contrastive multiview
learning to capture mutual information across views, leading to more
robust and generalizable representations for spectral annotation.
Unlike prior approaches that consider multiple views via concatenation
or as targets of auxiliary tasks, MVP learns from all views jointly,
resulting in improved molecular candidate ranking. Notably, MVP supports
annotation using either individual spectra or consensus spectra, enabling
flexible use of multiple measurements. On the MassSpecGym benchmark,
we show that annotation using query consensus spectra significantly
outperforms rank aggregation strategies based on constituent spectrum
annotation. Using the consensus spectrum view, MVP achieves 36.0 and
14.0% rank@1 when retrieving candidates by mass and formula, respectively.
When ranking using individual spectra, MVP demonstrates performance
that is superior to or on par with existing methods, achieving 26.4
and 11.1% rank@1 for candidates by mass and formula, respectively.
MVP offers a flexible, extensible foundation for learning from multiple
molecule/spectra data views.