Imagine an AI that could sift through mountains of dense pharmaceutical patents, instantly extracting key chemical structures and answering complex research questions. That’s the promise of PatentAgent, a new intelligent agent designed to revolutionize how scientists analyze drug patents. Pharmaceutical patents are a treasure trove of information for researchers, offering early access to crucial data long before it appears in publications. But navigating these complex documents is a Herculean task, often requiring painstaking manual review and specialized expertise. Existing computational tools offer piecemeal solutions, focusing on specific tasks like text mining or chemical structure identification, but lack an integrated approach. PatentAgent changes the game by offering a unified platform that combines the power of large language models (LLMs) with specialized modules for patent analysis. Its LLM “orchestrator” acts as a central command center, interpreting user queries and directing the workflow to the appropriate modules. Need to extract information from patent text? PatentAgent’s PA-QA module can handle that, acting as a chatbot that provides targeted answers to your research questions. What about deciphering complex chemical images embedded within patents? The PA-Img2Mol module steps in, converting these images into molecular structures using an advanced algorithm that outperforms existing methods. Finally, the PA-CoreId module tackles the critical task of identifying the core chemical structure within a patent, crucial for understanding the drug's mechanism of action and potential applications. Tests on benchmark datasets show that PatentAgent’s modules excel in their respective tasks. PA-Img2Mol achieves remarkable accuracy in converting chemical images to molecular structures, while PA-CoreId sets a new state-of-the-art in identifying core chemical structures. A real-world case study demonstrated how PatentAgent could significantly simplify the analysis of a complex pharmaceutical patent, saving researchers valuable time and effort. While promising, PatentAgent is still in its early stages. Future research will focus on refining its modules and expanding its capabilities to handle an even wider range of patent analysis tasks. Challenges remain, such as improving the accuracy of chemical image retrieval from patents and developing more comprehensive evaluation datasets. However, PatentAgent represents a significant step forward in leveraging AI for drug discovery, paving the way for faster, more efficient, and ultimately more successful pharmaceutical research.