# textttPatentAgent: Intelligent Agent for Automated Pharmaceutical Patent Analysis

arXiv: https://arxiv.org/abs/2410.21312 · HTML: https://mlsanity.com/paper/2410.21312/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 57 references that are in the map.
- First posted on arXiv 25 Oct 2024.

## Builds on (most cited first, 6 of 6)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Evaluating Large Language Models at Evaluating Instruction Following](https://mlsanity.com/paper/2310.07641/) (2023)
- [MolScribe: Robust Molecular Structure Recognition with Image-To-Graph Generation](https://mlsanity.com/paper/2205.14311/) (2022)
- [RxnScribe: A Sequence Generation Model for Reaction Diagram Parsing](https://mlsanity.com/paper/2305.11845/) (2023)
- [MolTailor: Tailoring Chemical Molecular Representation to Specific Tasks via Text Prompts](https://mlsanity.com/paper/2401.11403/) (2024)
- [LLM-based Extraction of Contradictions from Patents](https://mlsanity.com/paper/2403.14258/) (2024)

## Built on by (most cited first, 4 of 4)

- [From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review](https://mlsanity.com/paper/2504.19678/) (2025)
- [A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasures](https://mlsanity.com/paper/2506.19676/) (2025)
- [Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test?](https://mlsanity.com/paper/2507.10576/) (2025)
- [R-GroundBench: A Diagnostic Benchmark for R-Group Groundingin Markush Molecular Editing](https://mlsanity.com/paper/2610.00700/) (2026)

## Abstract (arXiv)

Pharmaceutical patents play a vital role in biochemical industries, especially in drug discovery, providing researchers with unique early access to data, experimental results, and research insights. With the advancement of machine learning, patent analysis has evolved from manual labor to tasks assisted by automatic tools. However, there still lacks an unified agent that assists every aspect of patent analysis, from patent reading to core chemical identification. Leveraging the capabilities of Large Language Models (LLMs) to understand requests and follow instructions, we introduce the first intelligent agent in this domain, textttPatentAgent, poised to advance and potentially revolutionize the landscape of pharmaceutical research. textttPatentAgent comprises three key end-to-end modules -- textitPA-QA, textitPA-Img2Mol, and textitPA-CoreId -- that respectively perform (1) patent question-answering, (2) image-to-molecular-structure conversion, and (3) core chemical structure identification, addressing the essential needs of scientists and practitioners in pharmaceutical patent analysis. Each module of textttPatentAgent demonstrates significant effectiveness with the updated algorithm and the synergistic design of textttPatentAgent framework. textitPA-Img2Mol outperforms existing methods across CLEF, JPO, UOB, and USPTO patent benchmarks with an accuracy gain between 2.46% and 8.37% while textitPA-CoreId realizes accuracy improvement ranging from 7.15% to 7.62% on PatentNetML benchmark. Our code and dataset will be publicly available.