arXiv AI

MAST: Motif-Augmented Diffusion with Search Tree for Spectroscopic Molecular Structure Elucidation

arXiv Machine Learning
Jul 23

Hypothesis-and-Refinement Learning of Organic Structures from Multimodal Spectroscopic Data

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
arXiv Machine Learning
Jul 28

FRIGID: Scaling Diffusion-Based Molecular Generation from Mass Spectra at Training and Inference Time

arXiv:2604. 16648v2 Announce Type: replace Abstract: Tandem mass spectrometry is prominent in scientific discovery workflows for identifying unknown small molecules, yet high-throughput structural elucidation remains challenging.

By Montgomery Bohde, Hongxuan Liu, Mrunali Manjrekar, Magdalena Lederbauer, Shuiwang Ji, Runzhong Wang, Connor W. Coley
arXiv Machine Learning
Jul 30

Data Fusion and Contrastive Alignment for Unconstrained IR Molecular Structure Elucidation

arXiv:2607. 26164v1 Announce Type: new Abstract: Automated molecular structure elucidation from infrared (IR) spectroscopy data has seen significant advancements in recent years, but its broad applicability is limited by a reliance on pre-determined chemical formulas provided as auxiliary model inputs.

By Ethan J. Mick, Campbell A. Sweet, Matthias J. Young, Derek T. Anderson
arXiv Machine Learning
4d ago

Explainable Molecular Structure Inference from GC--MS with Diffusion Models and LLM Reranking

The paper introduces DiffGCMS, a discrete graph diffusion model that generates molecular structures directly from GC–EI–MS spectra, and couples it with a large language model for post‑processing. In the first stage, DiffGCMS produces candidate structures; the LLM then validates, repairs, and reranks these candidates while offering interpretable explanations of fragment‑ion peaks. On a large NIST 20 test set, the combined approach improves accuracy and ensures 100% candidate validity for small molecules, demonstrating the value of spectrum‑aware post‑processing.

By Changlin Liu, Tianyu Yi, Chengchun Liu, Boxuan Zhao, Fanyang Mo
arXiv AI
Aug 18

Multi-Agent Closed-Loop Reasoning for Organic Structure Elucidation from Multimodal Spectra

arXiv:2608. 14720v1 Announce Type: cross Abstract: Following the molecular discovery and synthesis revolutions, scalable automated structure elucidation from routine spectroscopic data remains an outstanding challenge.

By Bingsen Xue, Zhuojun Jiang, Jianhao Zhang, Mingcheng Gu, Yizhe Yuan, Yongtai Zhuo, Yifan Zhang, Li Wang, Ya Su, Yue Yuan, Jiang Liu, Xueqian Kong, Cheng Jin
arXiv AI
2d ago

Inference-Time Projection for Physically Valid Biomolecular Diffusion Models

The paper introduces two closed‑form projection operators that enforce physical validity in biomolecular diffusion models during inference. One operator resolves inter‑chain van der Waals clashes, while the other restores ligand geometry, including bond lengths, angles, planarity, and chirality. These lightweight, model‑agnostic corrections preserve structural accuracy and ligand placement across multiple benchmarks without adding runtime or memory overhead.

By Qurat-ul-ain, Yee Whye Teh, Charlotte M. Deane, Matteo Cagiada