The paper presents a Bayesian inference framework for magnetic resonance spectroscopy (MRS) that employs Sylvester normalizing flows (SNFs) to approximate posterior distributions over metabolite concentrations. A physics-based decoder incorporates prior knowledge of MRS signal formation, ensuring realistic distribution representations. Validation on simulated 7T proton MRS data shows accurate metabolite quantification, well-calibrated uncertainties, and insights into parameter correlations and multi‑modal distributions.
By Julian P. Merkofer, Dennis M. J. van de Sande, Alex A. Bhogal, Ruud J. G. van Sloun
The paper introduces a semi‑supervised framework that learns to predict nuclear magnetic resonance (NMR) chemical shifts from millions of literature‑extracted spectra without explicit atom‑level assignments. By treating the prediction as a permutation‑invariant set supervision problem, the authors show that optimal bipartite matching can be reduced to a sorting‑based loss, enabling stable large‑scale training. The resulting models outperform state‑of‑the‑art methods, generalize better to diverse molecules, and for the first time capture systematic solvent effects across common NMR solvents.
By Yongqi Jin, Yecheng Wang, Jun-jie Wang, Rong Zhu, Guolin Ke, Weinan E
The paper introduces MSAlign, a lightweight model that aligns frozen foundation models for mass spectra (DreaMS) and molecules (MolDeBERTa) to improve metabolite identification from MS/MS spectra. It presents a unified framework for representation alignment and contrastive learning, demonstrates that a score fusion strategy further boosts performance at minimal cost, and addresses evaluation challenges by quantifying distribution shift in data splitting strategies. All resources, including datasets, splits, and code, are publicly released to promote reproducible research.
By Paul Krzakala, Gabriel Melo, Camille Lan\c{c}on, Charlotte Laclau, R\'emi Flamary, Etienne Th\'evenot, Florence d'Alch\'e-Buc
arXiv:2609.14360v2 Announce Type: replace
Abstract: Reliable small-molecule identification often requires complementary evidence from multiple spectroscopic measurements. In practice, however, spectr...
By Bowen Gao, Lei Zhu, Yiying Wang, Wenjie Yu
arXiv:2608. 13234v1 Announce Type: new Abstract: In order to understand complex systems such as the human metabolome or human brain, different sensing technologies are used, generating complex data.
By Gaute Johannessen, Geert Roelof van der Ploeg, Evrim Acar
arXiv:2512. 19733v3 Announce Type: replace-cross Abstract: Molecular structure elucidation from spectroscopic data is a long-standing challenge in Chemistry, traditionally requiring expert interpretation.
By Federico Ottomano, Yingzhen Li, Alex M. Ganose