LSM-MS2: A Foundation Model Bridging Spectral Identification and Biological Interpretation
This addresses the bottleneck in analyzing mass spectrometry data for biological and chemical research, offering a significant but incremental advance over prior methods.
The paper tackled the problem of uncharacterized mass spectrometry data by developing LSM-MS2, a foundation model that improved spectral identification accuracy by 30% for isomeric compounds and increased correct identifications by 42% in complex samples.
A vast majority of mass spectrometry data remains uncharacterized, leaving much of its biological and chemical information untapped. Recent advances in machine learning have begun to address this gap, particularly for tasks such as spectral identification in tandem mass spectrometry data. Here, we present the latest generation of LSM-MS2, a large-scale deep learning foundation model trained on millions of spectra to learn a semantic chemical space. LSM-MS2 achieves state-of-the-art performance in spectral identification, improving on existing methods by 30% in accuracy of identifying challenging isomeric compounds, yielding 42% more correct identifications in complex biological samples, and maintaining robustness under low-concentration conditions. Furthermore, LSM-MS2 produces rich spectral embeddings that enable direct biological interpretation from minimal downstream data, successfully differentiating disease states and predicting clinical outcomes across diverse translational applications.