采矿专利与大型语言模型阐明了化学功能的景观
Clayton W Kosonocky1, Claus O Wilke1, Edward M Marcotte1
1The University of Texas at Austin.
ArXiv
|January 10, 2024
概括
研究人员从专利创建了大量化学功能的数据集,以发现新的分子. 这种基于文本的方法补充了传统的基于结构的药物发现方法.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 小分子的发现旨在创造具有特定功能的化学物质.
- 传统方法通常依赖于基于结构的方法.
- 正在探索利用化学文献的直角方法.
研究的目的:
- 调查使用文本衍生化学函数数据的实用性.
- 要确定一个大型的文本衍生数据集是否反映了化学函数格局.
- 评估一种用于分子发现的新方法.
主要方法:
- 使用LLM和嵌入方法开发了一个化学函数 (CheF) 数据集.
- 从 188K 个独特的专利中提取了 ~ 100K 分子的功能标签.
- 创建了631K个分子功能对进行分析.
主要成果:
- CheF 数据集提供了化学函数的语义上连贯的文本表示.
- 数据集的功能格局接近于实际的化学函数格局.
- 开发并验证了一种预测仅从结构中预测功能配置文件的模型.
结论:
- 来自文本的功能性景观可以接近现实世界的化学功能.
- 功能标签引导的发现为基于结构的方法提供了一个直角的方法.
- 这种方法有助于设计新的功能分子.
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