mlsanityData to 7 Oct 2026

De Novo Molecular Generation from Mass Spectra via Many-Body Enhanced Diffusion

arXiv 2602.01643 ↗First posted 2 Feb 2026cs.LG cs.AIcited by 3

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Abstract (arXiv)

Molecular structure generation from mass spectrometry is fundamental for understanding cellular metabolism and discovering novel compounds. Although tandem mass spectrometry (MS/MS) enables the high-throughput acquisition of fragment fingerprints, these spectra often reflect higher-order interactions involving the concerted cleavage of multiple atoms and bonds-crucial for resolving complex isomers and non-local fragmentation mechanisms. However, most existing methods adopt atom-centric and pairwise interaction modeling, overlooking higher-order edge interactions and lacking the capacity to systematically capture essential many-body characteristics for structure generation. To overcome these limitations, we present MBGen, a Many-Body enhanced diffusion framework for de novo molecular structure Generation from mass spectra. By integrating a many-body attention mechanism and higher-order edge modeling, MBGen comprehensively leverages the rich structural information encoded in MS/MS spectra, enabling accurate de novo generation and isomer differentiation for novel molecules. Experimental results on the NPLIB1 and MassSpecGym benchmarks demonstrate that MBGen achieves superior performance, with improvements of up to 230% over state-of-the-art methods, highlighting the scientific value and practical utility of many-body modeling for mass spectrometry-based molecular generation. Further analysis and ablation studies show that our approach effectively captures higher-order interactions and exhibits enhanced sensitivity to complex isomeric and non-local fragmentation information.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 7 of its 31 references that are in the map. First posted on arXiv 2 Feb 2026.

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7 of 31 references.

References · 7

  1. 2017FiLM: Visual Reasoning with a General Conditioning Layercited by 2,286
  2. 2018Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Modelscited by 270
  3. 2021Global Self-Attention as a Replacement for Graph Convolutioncited by 113
  4. 2024MassSpecGym: A benchmark for the discovery and identification of moleculescited by 36
  5. 2025DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectracited by 28
  6. 2024Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformerscited by 12
  7. 2022GEM-2: Next Generation Molecular Property Prediction Network by Modeling Full-range Many-body Interactionscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.