Mass Spectrometry

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2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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dIon: Fragmentation-Based Invariance for Self-Supervised Learning of Tandem Mass Spectra
Oct 5, 2026Alfred Nilsson, Joel Lapin, Samuel H. Payne +2Self-Supervised Pre-Training Mass Spectrometry
Science for Life Laboratory, KTH Royal Institute of Technology Stockholm, Sweden · Computational Mass Spectrometry, TUM School of Life Sciences Technical University of Munich, Freising, Germany · Biology Department, Brigham Young University Provo, Utah 84602, United States +1
Transferable Mass Spectrum Prediction via Reference-Guided Test-time Specialization
Sep 28, 2026Yunhua Zhong, Runting Li, Yifan Li +5Test-Time Adaptation Continual Test-Time Adaptation
The Hong Kong University of Science and Technology (Guangzhou) · The University of Hong Kong · South China University of Technology +3
Predicting Collision Cross Sections with GRACE: Geometric Residual Adduct Conditioning via Early-fusion
Sep 14, 2026Parthasarathy Suryanarayanan, Susanta Das, Shreyans Sethi +3Molecular Property Prediction Mass Spectrometry
IBM Research, Yorktown Heights, NY 10598, United States · Center for Computational Life Sciences, Cleveland Clinic Research, The Cleveland Clinic, Cleveland, OH 44195, United States · Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
Biologically Informed Representation Learning for Robust Cross-Center Generalization of MALDI-TOF Mass Spectrometry
Aug 8, 2026Alejandro L. García-Navarro, Carlos Sevilla-Salcedo, Belén Rodríguez-Sánchez +1Domain Generalization Mass Spectrometry
Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Leganés, 28911, Spain · Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, 28009 Spain · Clinical Microbiology and Infectious Diseases Department at Hospital General Universitario Gregorio Marañón, Madrid, 28007, Spain
MS-GPT: Rethinking MS/MS De Novo Structure Elucidation as Spectrum-Induced Posterior Querying of a Molecule-Language Model
Jul 26, 2026Xin Zhao, Yumin Liu, Zhuo Li +5Language Modeling Molecular Generation
Agent-Guided Relational Concept Discovery: Toward Interpretable Surgical Margin Assessment
Jul 23, 2026Nooshin Maghsoodi, Amoon Jamzad, Robert Policelli +11Computational Pathology Mass Spectrometry
Queen’s University, Kingston, ON, Canada
A multiverse-consensus pipeline for reproducible feature selection in untargeted LC-MS metabolomics
Jul 19, 2026Mohammed Saeed Al-Huraibi, Ihsan Yozgat, Ahmet KaplanFeature Selection Mass Spectrometry
1Istanbul Medipol University, Istanbul, Turkey · 2Istinye University, Istanbul, Turkey
MARLIN: De Novo Molecular Structure Elucidation from Tandem Mass Spectra without a Ground-Truth Formula
Jul 6, 2026Xujun Che, Xiuxia Du, Depeng XuMolecular Generation Mass Spectrometry
Department of Software and Information Systems, University of North Carolina at Charlotte, Charlotte, NC 28223 · Center for Environmental Monitoring and Informatics Technologies for Public Health, University of North Carolina at Charlotte, Charlotte, NC 28223 · Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223
Self-Supervised Calibration of Scientific Instruments Using Physical Consistency Constraints
Jun 28, 2026M. Rejmund, A. LemassonSelf-Supervised Learning Physics-Informed ML
GANIL, CEA/DRF - CNRS/IN2P3, Bd Henri Becquerel, BP 55027, F-14076, Caen Cedex 5, France
GLACIER: Rethinking Mass Spectrum Prediction as an Object Detection Problem
Jun 28, 2026Rui-Xi Wang, Runzhong Wang, Connor W. ColeyMass Spectrometry
Massachusetts Institute of Technology Cambridge, MA 02139
MassSpecGym in the Wild: Uncovering and Correcting Evaluation Pitfalls in AI-Driven Molecule Discovery
Jun 17, 2026Hongxuan Liu, Roman Bushuiev, Ivy Lightheart +12Benchmark Design Benchmark Contamination
1Massachusetts Institute of Technology · Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences · 3Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague +2
MemNovo: Look Back at the Spectrum for Balanced De Novo Peptide Sequencing from Mass Spectrometry
Jun 10, 2026Dongxin Lyu, Jingbo Zhou, Hongxin Xiang +2Memory-Augmented Neural Networks Transformer Inference
Westlake University Hangzhou, Zhejiang, China · Hunan University Changsha, Hunan, China · Shanghai Artificial Intelligence Laboratory Shanghai, China +1
The Language of Elution: Autoregressive Prediction of the Next Feature in Untargeted LC-HRMS Lipidomics
Jun 2, 2026Dayanjan S. WijesingheMass Spectrometry Sequence Modeling
Department of Pharmacotherapy and Outcomes Sciences, Virginia Commonwealth University School of2026 Pharmacy, Richmond, VA 23298, USA
SCENT: Aligning Mass Spectra with Molecular Structure for Olfactory Perception
May 26, 2026Ziqi Zhang, Eunyeong Jin, Miguel Vasco +6Cross-Modal Alignment Molecular Property Prediction
1Dept. of Intelligent Systems, KTH Royal Institute of Technology, Stockholm, Sweden · 2Atmospheric Chemistry Dept., Max Planck Institute for Chemistry, Mainz, Germany · 3Dept. of Information Technology, Uppsala University, Uppsala, Sweden +1
MSAlign: Aligning Molecule and Mass Spectra Foundation Models for Metabolite Identification
May 19, 2026Paul Krzakala, Gabriel Melo, Camille Lançon +4Distribution Shift Cross-Modal Alignment
LTCI, Télécom Paris & CMAP, Ecole Polytechnique, Institut Polytechnique de Paris · LTCI, Télécom Paris, Institut Polytechnique de Paris · CEA, INRAE, MetaboHUB, Université Paris-Saclay +1
CoRe-Gen: Robust Spectrum-to-Structure Generation under Imperfect Fingerprint Conditions
May 13, 2026Tianbo Liu, Chixiang Lu, Jing Hao +4Autoregressive Generation Molecular Generation
The University of Hong Kong · The Chinese University of Hong Kong · Zhejiang Shuren University
A Flexible Adaptive Stable Clustering Algorithm for Archive-Scale Online Mass Spectrometry
May 8, 2026Shao Shi, Xin Yang, Huiran Feng +8Algorithmic Stability Clustering
Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen, 518055, Guangdong, China. · Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen, 518055, Guangdong, China.
Unlocking High-Fidelity Molecular Generation from Mass Spectra via Dual-Stream Line Graph Diffusion
May 7, 2026Xujun Che, Xiuxia Du, Depeng XuGraph Neural Networks Molecular Graph Generation
Department of Software and Information Systems,2026 University of North Carolina at Charlotte, Charlotte, NC 28223 · Center for Environmental Monitoring and Informatics Technologies for Public Health University of North Carolina at Charlotte, Charlotte, NC 28223 · Department of Bioinformatics and Genomics,May University of North Carolina at Charlotte, Charlotte, NC 28223
PepSpecBench: A Unified Evaluation Benchmark for Peptide Tandem Mass Spectrometry Prediction
May 3, 2026Zhiwen Yang, Pan Liu, Yifan Li +2Benchmark Design Benchmark Contamination
The Hong Kong University of Science and Technology (Guangzhou). · Yangzhou University. · The Hong Kong University of Science and Technology.
IonMorphNet: Generalizable Learning of Ion Image Morphologies for Peak Picking in Mass Spectrometry Imaging
Apr 21, 2026Philipp Weigand, Niels Nawrot, Nikolas Ebert +2Representation Learning Mass Spectrometry
Mannheim University of Applied Sciences, Germany · Faculty of Biosciences, Heidelberg University, Germany
FRIGID: Scaling Diffusion-Based Molecular Generation from Mass Spectra at Training and Inference Time
Apr 17, 2026Montgomery Bohde, Hongxuan Liu, Mrunali Manjrekar +4Inference-Time Scaling Molecular Generation
Massachusetts Institute of Technology, Cambridge, MA, United States · Texas A&M University, College Station, TX, United States
FlexMS: A Unified Public Benchmark for Molecule Tandem Mass Spectrum Prediction
Feb 26, 2026Yunhua Zhong, Yixuan Tang, Yifan Li +5Benchmark Design Mass Spectrometry
The Hong Kong University of Science and Technology (Guangzhou). · The University of Hong Kong. · Yangzhou University. +3

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dIon: Fragmentation-Based Invariance for Self-Supervised Learning of Tandem Mass Spectra

Alfred Nilsson, Joel Lapin, Samuel H. Payne, Mathias Wilhelm+1

Science for Life Laboratory, KTH Royal Institute of Technology Stockholm, Sweden · Computational Mass Spectrometry, TUM School of Life Sciences Technical University of Munich, Freising, Germany · Biology Department, Brigham Young University Provo, Utah 84602, United States+1

Self-Supervised Pre-Training Mass Spectrometry Invariant Representation Learning Molecular Representation Learning

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We introduce a novel invariance for peptide tandem mass spectrometry data, unlocking self-supervised representation learning that improves de novo sequencing of peptides. This invariance exploits the physical relationship between precursor properties (mass and charge) and fragment-ion evidence, without requiring peptide sequence labels. We introduce dIon, which adapts the DINO framework with two latent prediction tasks, both recovering a clean teacher representation: one from a spectrum mixture, using the precursor as a selection query, and one from a partial spectrum with the precursor withheld. The first associates precursor information with fragment-ion evidence; the second prevents representational collapse onto that information alone. Mechanistic probes support both effects, and ablations show that the full objective performs best. Under identical end-to-end training, dIon initialization improves de novo peptide precision over training from scratch by 5.5 and 8.4 percentage points on the held-out MassIVE-KB and Kingdoms test sets, and by 2.3 and 4.8 percentage points with a larger supervised training corpus. The resulting models surpass fully supervised state-of-the-art de novo sequencing models on the diverse, multi-species Kingdoms corpus under the same greedy-decoding protocol. Without peptide labels, dIon learns strong native peptide-similarity geometry compared with other learned models; with limited peptide-supervised adaptation, it achieves the best retrieval and pair-discrimination performance across all representation benchmarks.

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Latest papers 22