[2601.17204] SpecBridge: Bridging Mass Spectrometry and Molecular Representations via Cross-Modal Alignment

Computer Science > Machine Learning arXiv:2601.17204 (cs) This paper has been withdrawn by Yinkai Wang [Submitted on 23 Jan 2026 (v1), last revised 3 Mar 2026 (this version, v3)] Title:SpecBridge: Bridging Mass Spectrometry and Molecular Representations via Cross-Modal Alignment Authors:Yinkai Wang, Yan Zhou Chen, Xiaohui Chen, Li-Ping Liu, Soha Hassoun View a PDF of the paper titled SpecBridge: Bridging Mass Spectrometry and Molecular Representations via Cross-Modal Alignment, by Yinkai Wang and 4 other authors No PDF available, click to view other formats 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 stro...