@inproceedings{zhuang-etal-2025-boosting,
title = "Boosting {LLM}{'}s Molecular Structure Elucidation with Knowledge Enhanced Tree Search Reasoning",
author = "Zhuang, Xiang and
Wu, Bin and
Cui, Jiyu and
Feng, Kehua and
Li, Xiaotong and
Xing, Huabin and
Ding, Keyan and
Zhang, Qiang and
Chen, Huajun",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1100/",
doi = "10.18653/v1/2025.acl-long.1100",
pages = "22561--22576",
ISBN = "979-8-89176-251-0",
abstract = "Molecular structure elucidation involves deducing a molecule{'}s structure from various types of spectral data, which is crucial in chemical experimental analysis. While large language models (LLMs) have shown remarkable proficiency in analyzing and reasoning through complex tasks, they still encounter substantial challenges in molecular structure elucidation. We identify that these challenges largely stem from LLMs' limited grasp of specialized chemical knowledge. In this work, we introduce a Knowledge-enhanced reasoning framework for Molecular Structure Elucidation (K-MSE), leveraging Monte Carlo Tree Search for test-time scaling as a plugin. Specifically, we construct an external molecular substructure knowledge base to extend the LLMs' coverage of the chemical structure space. Furthermore, we design a specialized molecule-spectrum scorer to act as a reward model for the reasoning process, addressing the issue of inaccurate solution evaluation in LLMs. Experimental results show that our approach significantly boosts performance, particularly gaining more than 20{\%} improvement on both GPT-4o-mini and GPT-4o."
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<abstract>Molecular structure elucidation involves deducing a molecule’s structure from various types of spectral data, which is crucial in chemical experimental analysis. While large language models (LLMs) have shown remarkable proficiency in analyzing and reasoning through complex tasks, they still encounter substantial challenges in molecular structure elucidation. We identify that these challenges largely stem from LLMs’ limited grasp of specialized chemical knowledge. In this work, we introduce a Knowledge-enhanced reasoning framework for Molecular Structure Elucidation (K-MSE), leveraging Monte Carlo Tree Search for test-time scaling as a plugin. Specifically, we construct an external molecular substructure knowledge base to extend the LLMs’ coverage of the chemical structure space. Furthermore, we design a specialized molecule-spectrum scorer to act as a reward model for the reasoning process, addressing the issue of inaccurate solution evaluation in LLMs. Experimental results show that our approach significantly boosts performance, particularly gaining more than 20% improvement on both GPT-4o-mini and GPT-4o.</abstract>
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%0 Conference Proceedings
%T Boosting LLM’s Molecular Structure Elucidation with Knowledge Enhanced Tree Search Reasoning
%A Zhuang, Xiang
%A Wu, Bin
%A Cui, Jiyu
%A Feng, Kehua
%A Li, Xiaotong
%A Xing, Huabin
%A Ding, Keyan
%A Zhang, Qiang
%A Chen, Huajun
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F zhuang-etal-2025-boosting
%X Molecular structure elucidation involves deducing a molecule’s structure from various types of spectral data, which is crucial in chemical experimental analysis. While large language models (LLMs) have shown remarkable proficiency in analyzing and reasoning through complex tasks, they still encounter substantial challenges in molecular structure elucidation. We identify that these challenges largely stem from LLMs’ limited grasp of specialized chemical knowledge. In this work, we introduce a Knowledge-enhanced reasoning framework for Molecular Structure Elucidation (K-MSE), leveraging Monte Carlo Tree Search for test-time scaling as a plugin. Specifically, we construct an external molecular substructure knowledge base to extend the LLMs’ coverage of the chemical structure space. Furthermore, we design a specialized molecule-spectrum scorer to act as a reward model for the reasoning process, addressing the issue of inaccurate solution evaluation in LLMs. Experimental results show that our approach significantly boosts performance, particularly gaining more than 20% improvement on both GPT-4o-mini and GPT-4o.
%R 10.18653/v1/2025.acl-long.1100
%U https://aclanthology.org/2025.acl-long.1100/
%U https://doi.org/10.18653/v1/2025.acl-long.1100
%P 22561-22576
Markdown (Informal)
[Boosting LLM’s Molecular Structure Elucidation with Knowledge Enhanced Tree Search Reasoning](https://aclanthology.org/2025.acl-long.1100/) (Zhuang et al., ACL 2025)
ACL
- Xiang Zhuang, Bin Wu, Jiyu Cui, Kehua Feng, Xiaotong Li, Huabin Xing, Keyan Ding, Qiang Zhang, and Huajun Chen. 2025. Boosting LLM’s Molecular Structure Elucidation with Knowledge Enhanced Tree Search Reasoning. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 22561–22576, Vienna, Austria. Association for Computational Linguistics.