SpecBridge: Bridging Mass Spectrometry and Molecular Representations via Cross-Modal Alignment

TL;DR

SpecBridge introduces a novel approach for small-molecule identification by aligning spectral data with molecular representations through a geometric alignment framework, significantly improving retrieval accuracy while maintaining model simplicity.

Contribution

It proposes an implicit alignment method that fine-tunes a spectral encoder to directly project into a frozen molecular model's latent space, offering a practical alternative to complex neural architectures.

Findings

20-25% improvement in top-1 retrieval accuracy

Effective alignment to frozen foundation models

Small number of trainable parameters

Abstract

Small-molecule identification from tandem mass spectrometry (MS/MS) remains a bottleneck in untargeted settings where spectral libraries are incomplete. While deep learning offers a solution, current approaches typically fall into two extremes: explicit generative models that construct molecular graphs atom-by-atom, or joint contrastive models that learn cross-modal subspaces from scratch. We introduce SpecBridge, a novel implicit alignment framework that treats structure identification as a geometric alignment problem. SpecBridge fine-tunes a self-supervised spectral encoder (DreaMS) to project directly into the latent space of a frozen molecular foundation model (ChemBERTa), and then performs retrieval by cosine similarity to a fixed bank of precomputed molecular embeddings. Across MassSpecGym, Spectraverse, and MSnLib benchmarks, SpecBridge improves top-1 retrieval accuracy by roughly 20-25% relative to strong neural baselines, while keeping the number of trainable parameters small. These results suggest that aligning to frozen foundation models is a practical, stable alternative to designing new architectures from scratch. The code for SpecBridge is released at https://github.com/HassounLab/SpecBridge.