# ChemMiner: A Large Language Model Agent System for Chemical Literature Data Mining

arXiv: https://arxiv.org/abs/2402.12993 · HTML: https://mlsanity.com/paper/2402.12993/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 3 of its 25 references that are in the map.
- First posted on arXiv 20 Feb 2024.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 3 of 3)

- [Developing ChemDFM as a large language foundation model for chemistry](https://mlsanity.com/paper/2401.14818/) (2024)
- [LLMind: Orchestrating AI and IoT with LLM for Complex Task Execution](https://mlsanity.com/paper/2312.09007/) (2023)
- [The Power of Large Language Models for Wireless Communication System Development: A Case Study on FPGA Platforms](https://mlsanity.com/paper/2307.07319/) (2023)

## Built on by (most cited first, 7 of 7)

- [A Review of Large Language Models and Autonomous Agents in Chemistry](https://mlsanity.com/paper/2407.01603/) (2024)
- [From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery](https://mlsanity.com/paper/2508.14111/) (2025)
- [AutoLabs: Cognitive Multi-Agent Systems with Self-Correction for Autonomous Chemical Experimentation](https://mlsanity.com/paper/2509.25651/) (2025)
- [El Agente Quntur: A research collaborator agent for quantum chemistry](https://mlsanity.com/paper/2602.04850/) (2026)
- [OD-MoE: On-Demand Expert Loading for Cacheless Edge-Distributed MoE Inference](https://mlsanity.com/paper/2512.03927/) (2025)
- [ChemVA: Advancing Large Language Models on Chemical Reaction Diagrams Understanding](https://mlsanity.com/paper/2605.17214/) (2026)
- [SALSA: Semi-Autonomous Literature Summarization Assistant](https://mlsanity.com/paper/2609.22210/) (2026)

## Abstract (arXiv)

The development of AI-assisted chemical synthesis tools requires comprehensive datasets covering diverse reaction types, yet current high-throughput experimental (HTE) approaches are expensive and limited in scope. Chemical literature represents a vast, underexplored data source containing thousands of reactions published annually. However, extracting reaction information from literature faces significant challenges including varied writing styles, complex coreference relationships, and multimodal information presentation. This paper proposes ChemMiner, a novel end-to-end framework leveraging multiple agents powered by large language models (LLMs) to extract high-fidelity chemical data from literature. ChemMiner incorporates three specialized agents: a text analysis agent for coreference mapping, a multimodal agent for non-textual information extraction, and a synthesis analysis agent for data generation. Furthermore, we developed a comprehensive benchmark with expert-annotated chemical literature to evaluate both extraction efficiency and precision. Experimental results demonstrate reaction identification rates comparable to human chemists while significantly reducing processing time, with high accuracy, recall, and F1 scores. Our open-sourced benchmark facilitates future research in chemical literature data mining.