A Multi-Agent System Enables Versatile Information Extraction from the Chemical Literature
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910043088093184 |
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| author | Chen, Yufan Leung, Ching Ting Yu, Bowen Sun, Jianwei Huang, Yong Li, Linyan Chen, Hao Gao, Hanyu |
| author_facet | Chen, Yufan Leung, Ching Ting Yu, Bowen Sun, Jianwei Huang, Yong Li, Linyan Chen, Hao Gao, Hanyu |
| contents | To fully expedite AI-powered chemical research, high-quality chemical databases are the foundation. Automatic extraction of chemical information from the literature is essential for constructing reaction databases, but it is currently limited by the multimodality and style variability of chemical information. In this work, we developed a multimodal large language model (MLLM)-based multi-agent system for robust and automated chemical information extraction. It utilizes the MLLM's strong reasoning capability to understand the structure of diverse chemical graphics and decompose the extraction task into sub-tasks. It then coordinates a set of specialized agents, each combining the capabilities of the MLLM with the precise, domain-specific strengths of dedicated tools and web services, to solve the subtasks accurately and integrate the results into a unified output. Our system achieved an F1 score of 76.27% on a benchmark dataset of sophisticated multimodal chemical reaction graphics from the literature, surpassing the previous state-of-the-art model (F1 score of 39.13%) by a significant margin. Additionally, it demonstrated versatile applicability in a range of other information extraction tasks, including molecular image recognition, reaction image parsing, named entity recognition and text-based reaction extraction. This work is a critical step toward automated chemical information extraction into structured datasets, which will be a strong promoter of AI-driven chemical research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20230 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Multi-Agent System Enables Versatile Information Extraction from the Chemical Literature Chen, Yufan Leung, Ching Ting Yu, Bowen Sun, Jianwei Huang, Yong Li, Linyan Chen, Hao Gao, Hanyu Artificial Intelligence Computer Vision and Pattern Recognition Multiagent Systems To fully expedite AI-powered chemical research, high-quality chemical databases are the foundation. Automatic extraction of chemical information from the literature is essential for constructing reaction databases, but it is currently limited by the multimodality and style variability of chemical information. In this work, we developed a multimodal large language model (MLLM)-based multi-agent system for robust and automated chemical information extraction. It utilizes the MLLM's strong reasoning capability to understand the structure of diverse chemical graphics and decompose the extraction task into sub-tasks. It then coordinates a set of specialized agents, each combining the capabilities of the MLLM with the precise, domain-specific strengths of dedicated tools and web services, to solve the subtasks accurately and integrate the results into a unified output. Our system achieved an F1 score of 76.27% on a benchmark dataset of sophisticated multimodal chemical reaction graphics from the literature, surpassing the previous state-of-the-art model (F1 score of 39.13%) by a significant margin. Additionally, it demonstrated versatile applicability in a range of other information extraction tasks, including molecular image recognition, reaction image parsing, named entity recognition and text-based reaction extraction. This work is a critical step toward automated chemical information extraction into structured datasets, which will be a strong promoter of AI-driven chemical research. |
| title | A Multi-Agent System Enables Versatile Information Extraction from the Chemical Literature |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition Multiagent Systems |
| url | https://arxiv.org/abs/2507.20230 |