采矿专利与大型语言模型阐明了化学功能景观
Clayton W Kosonocky1, Claus O Wilke2, Edward M Marcotte1,3
1Department of Molecular Biosciences, University of Texas at Austin Austin TX 78705 USA.
Digital discovery
|June 14, 2024
概括
这项研究表明,化学文献可以指导小分子的发现. 一个新的数据集揭示了基于文本的化学功能,可以预测分子性质和识别具有所需功能的药物.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 化学信息学 化学信息学
背景情况:
- 小分子的发现传统上依赖于基于结构的方法.
- 利用广的化学文献提供了一个补充的方法.
- 化学功能与分子结构和相互作用密切相关.
研究的目的:
- 调查使用文本衍生数据用于化学函数预测的可行性.
- 从文献中开发和评估一个大规模的分子功能关系数据集.
- 为了证明基于文本的功能景观在药物发现中的实用性.
主要方法:
- 使用LLM和对专利数据的嵌入技术构建化学函数 (CheF) 数据集.
- 从188K项专利中提取了1.5K个独特的功能标签,用于~100K个分子.
- 分析语义连贯性和与化学结构关系的一致性.
主要成果:
- CheF数据集提供了化学功能的语义连贯表示.
- 数据集接近于实际的化学函数格局.
- 一个模型成功地从结构单独预测了功能概况,识别了具有目标功能的药物.
结论:
- 来自文本的功能景观可以有效地指导分子发现.
- 这种方法为传统的基于结构的方法提供了可行的替代方案.
- 功能标签引导的发现促进了新型功能分子的设计.
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