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
arXiv:2603. 23101v3 Announce Type: replace Abstract: Intelligent spectroscopy serves as a pivotal element in AI-driven closed-loop scientific discovery, functioning as the critical bridge between matter structure and artificial intelligence.
By Yutang Ge, Yaning Cui, Hanzheng Li, Jun-Jie Wang, Fanjie Xu, Jinhan Dong, Yongqi Jin, Dongxu Cui, Peng Jin, Guojiang Zhao, Hengxing Cai, Tianci Yangfeng, Xueqing Chen, Hongshuai Wang, Rong Zhu, Linfeng Zhang, Xiaohong Ji, Zhifeng Gao
arXiv:2512. 18531v2 Announce Type: replace-cross Abstract: One-dimensional NMR spectroscopy is one of the most widely used techniques for the characterization of organic compounds and natural products.
By Frank Hu, Jonathan M. Tubb, Dimitris Argyropoulos, Sergey Golotvin, Mikhail Elyashberg, Grant M. Rotskoff, Matthew W. Kanan, Thomas E. Markland
In order to understand complex systems such as the human metabolome or human brain, different sensing technologies are used, generating complex data. These datasets are often multiway, i.
arXiv:2609.14360v1 Announce Type: new
Abstract: In practical molecular characterization, small-molecule structure identification benefits from complementary spectroscopic evidence, but missing, degra...
By Bowen Gao, Lei Zhu, Yiying Wang, Wenjie Yu
arXiv:2607. 19816v1 Announce Type: cross Abstract: Determining molecular structures from spectroscopic data remains fundamentally challenging because the inverse problem is intrinsically underdetermined: individual spectra are sparse, low-dimensional, and encode only partial structural evidence relative to the vast space of possible molecules.
By Chengchun Liu, Zhiyuan Yan, Li Yuan, Hao Li, Boxuan Zhao, Yonghong Tian, Bartosz A. Grzybowski, Fanyang Mo
MM‑Spectrum is a sparse Mixture‑of‑Experts framework designed to infer molecular structures from multimodal spectroscopic data. It introduces a modality‑aware routing mechanism that exposes spectral identity to the router, along with shared and interaction experts of heterogeneous capacities to capture both modality‑unique and cross‑modal synergistic information while reducing noise interference. Experiments across full‑modality, bimodal, and missing‑modality scenarios show consistent and substantial performance gains, supported by ablation studies and interpretability analyses.
By Hai-tao Yu, Nan Min, Zheng Fang, Hongyu Zhan, Yusen Tan, Yuhan Wang, Jun Xia