Summary of Intelligent System For Automated Molecular Patent Infringement Assessment, by Yaorui Shi et al.
Intelligent System for Automated Molecular Patent Infringement Assessment
by Yaorui Shi, Sihang Li, Taiyan Zhang, Xi Fang, Jiankun Wang, Zhiyuan Liu, Guojiang Zhao, Zhengdan Zhu, Zhifeng Gao, Renxin Zhong, Linfeng Zhang, Guolin Ke, Weinan E, Hengxing Cai, Xiang Wang
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel multi-agent intelligence system, called PatentFinder, is introduced in this paper to accurately evaluate small molecules for patent infringement in automated drug discovery. This system combines five specialized agents with heuristic and model-based tools to generate interpretable reports on potential patent infringement. The authors curate a benchmark dataset, MolPatent-240, tailored for patent infringement assessment algorithms and demonstrate that PatentFinder outperforms baseline methods by achieving a 13.8% improvement in F1-score and a 12% increase in accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Automated drug discovery is trying to use machines to find new medicines instead of people. But this can cause problems because the new medicines might accidentally break existing patents, which could be expensive and complicated to fix. This paper makes a system called PatentFinder that helps figure out if new medicines infringe on old patents. It does this by using many tools and computer models to look at patent claims and medicine structures. The authors also made a special set of examples for testing this system and it did better than other methods. |
Keywords
» Artificial intelligence » F1 score
