Hugging Face Trending Papers

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

MAST is a motif‑augmented diffusion framework that uses a search tree to jointly infer 2D and 3D molecular structures from spectroscopic data. By incorporating explicit motif priors as intermediate evidence during denoising, it reduces spectral ambiguity and improves spectra‑conditioned optimization. The method casts diffusion sampling as a reward‑guided tree search, yielding a compact set of spectra‑consistent candidates with lower computational cost and achieving 94.89 % exact recovery on the QM9S benchmark while maintaining high chemical validity.

arXiv AI

Oct 9

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

MAST is a motif‑augmented diffusion framework that uses a search tree to jointly elucidate 2D and 3D molecular structures from spectroscopic data. It incorporates explicit motif priors as intermediate evidence during denoising, reducing spectral ambiguity, and casts diffusion sampling as a reward‑guided tree search to prioritize high‑reward trajectories, thereby limiting computational overhead. On the QM9S multi‑spectra benchmark, MAST achieves 94.89% exact recovery, improves 3D fidelity, and maintains high chemical validity and stability.

By Chenghao Jia, Mengdi Liu, Hong Chang, Shiguang Shan, Xilin Chen

diffusion benchmarks

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

llms diffusion efficiency benchmarks

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

fine-tuning multimodal benchmarks

arXiv Machine Learning

Jul 14

NMIRacle: Multi-modal Generative Molecular Elucidation from IR and NMR Spectra

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

rag fine-tuning multimodal

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

agents multimodal

Hugging Face Trending Papers

Oct 2

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

GC--EI--MS is an important technique for analyzing volatile and semivolatile compounds in complex samples. However, conventional methods rely heavily on reference spectral library matching, limiting t...

llms rag diffusion

arXiv Machine Learning

Oct 5

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

llms rag diffusion

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

llms safety

arXiv Machine Learning

Jul 7

MARLIN: De Novo Molecular Structure Elucidation from Tandem Mass Spectra without a Ground-Truth Formula

arXiv:2607. 04774v1 Announce Type: new Abstract: Untargeted tandem mass spectrometry (MS/MS) detects thousands of small molecules per biological sample, yet most go unidentified because they are absent from spectral libraries.

By Xujun Che, Xiuxia Du, Depeng Xu

llms diffusion benchmarks

Hugging Face Trending Papers

Sep 15

QALPA: Property-guided diffusion modeling for efficient exploration of chemical spaces of flexible molecules

Exploring the chemical space of flexible molecules remains challenging because the vast number of possible compounds and conformations, together with the increasing cost and limited generalization of...

diffusion

arXiv Machine Learning

Sep 1

S3C-LLM: Skill-Code Guided Agentic Language Models for Spectrum-to-Structure Elucidation

arXiv:2608.30910v1 Announce Type: new Abstract: Spectroscopic structure elucidation is central to molecular analysis, but recent Large Language Model (LLM)-based methods mostly formulate it as direct...

By Xuanle Zhao, Xinyuan Cai, Xiang Cheng, Bo Xu

llms agents reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jul 28

MS-GPT: Rethinking MS/MS De Novo Structure Elucidation as Spectrum-Induced Posterior Querying of a Molecule-Language Model

arXiv:2607. 23607v1 Announce Type: new Abstract: Molecular structure elucidation from tandem mass spectra (MS/MS) is a central inverse problem in analytical chemistry.

By Xin Zhao, Yumin Liu, Zhuo Li, Weichu Zheng, Feng Zhu, Xiaokang Yang, Yaohui Jin, Yanyan Xu

llms fine-tuning safety

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