用知识增强树搜索提升大模型解析分子结构的能力
Boosting LLM's Molecular Structure Elucidation with Knowledge Enhanced Tree Search Reasoning
- 构建外部子结构知识库扩展模型化学空间覆盖
- 引入分子-光谱评分器提升解题评估准确性，性能提升超20%
- 适合需要高精度分子结构推断的药物研发与化学分析场景
分子结构解析是从多种光谱数据中推断分子结构的关键任务，对化学实验分析至关重要。尽管大语言模型在复杂任务推理中表现出色，但在分子结构解析方面仍面临显著挑战，主要源于其对专业化学知识掌握有限。本文提出一种知识增强型分子结构解析框架K-MSE，利用蒙特卡洛树搜索实现测试时扩展。具体地，构建外部分子子结构知识库以扩展模型对化学结构空间的覆盖；设计专用分子-光谱评分器作为推理过程的奖励模型，解决大模型解题评估不准的问题。实验结果表明，该方法显著提升性能，尤其在GPT-4o-mini和GPT-4o上均取得超过20%的改进。代码已开源。
原文摘要 · Abstract (English)
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. Our code is available at https://github.com/HICAI-ZJU/K-MSE.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。