Intelligent System for Automated Molecular Patent Infringement Assessment
🤖 AI Summary
AI-driven drug discovery faces significant risks of molecular patent infringement, threatening the scalability and legal safety of automated workflows. To address this, we propose PatentFinder—a novel multi-agent system enabling fully automated patent infringement risk assessment for AI-generated small molecules. PatentFinder employs a five-agent collaborative architecture that integrates claim parsing, molecular structure comparison (using RDKit), and joint structural-semantic reasoning, augmented by heuristic rules and LLM-powered tools to generate interpretable, auditable infringement reports. Our contributions are threefold: (1) the first domain-specific benchmark, MolPatent-240, comprising 240 patent-protected molecules with expert-annotated infringement labels; (2) a patent-aware molecular representation and reasoning paradigm; and (3) state-of-the-art performance on MolPatent-240, achieving +13.8% F1-score and +12% accuracy over prior methods—substantially improving legal utility and interpretability.
Application Category
📝 Abstract
Automated drug discovery offers significant potential for accelerating the development of novel therapeutics by substituting labor-intensive human workflows with machine-driven processes. However, molecules generated by artificial intelligence may unintentionally infringe on existing patents, posing legal and financial risks that impede the full automation of drug discovery pipelines. This paper introduces PatentFinder, a novel multi-agent and tool-enhanced intelligence system that can accurately and comprehensively evaluate small molecules for patent infringement. PatentFinder features five specialized agents that collaboratively analyze patent claims and molecular structures with heuristic and model-based tools, generating interpretable infringement reports. To support systematic evaluation, we curate MolPatent-240, a benchmark dataset tailored for patent infringement assessment algorithms. On this benchmark, PatentFinder outperforms baseline methods that rely solely on large language models or specialized chemical tools, achieving a 13.8% improvement in F1-score and a 12% increase in accuracy. Additionally, PatentFinder autonomously generates detailed and interpretable patent infringement reports, showcasing enhanced accuracy and improved interpretability. The high accuracy and interpretability of PatentFinder make it a valuable and reliable tool for automating patent infringement assessments, offering a practical solution for integrating patent protection analysis into the drug discovery pipeline.
Problem
Research questions and friction points this paper is trying to address.
Artificial Intelligence
Molecular Patent Infringement
Automated Drug Discovery
Innovation
Methods, ideas, or system contributions that make the work stand out.
PatentFinder
Molecular Patent Infringement
Automated Detection
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