在化学相似性向量搜索中使用替代的SMILES表示来识别新的功能类型
Clayton W Kosonocky1, Aaron L Feller1, Claus O Wilke2
1Department of Molecular Biosciences, University of Texas at Austin, Austin, TX 78705, USA.
Patterns (New York, N.Y.)
|December 18, 2023
概括
这项研究引入了一种新的基于矢量的化学搜索,使用化学语言模型. 它识别了具有不同结构的功能上相似的药物,改善了制药发现.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 化学领域的人工智能
背景情况:
- 传统的化学相似性搜索依赖于基于结构的比较来识别制药.
- 现有的in silico方法在发现具有所需功能的新分子结构方面存在局限性.
研究的目的:
- 使用化学语言模型开发和评估一种新的基于矢量的化学搜索方法.
- 加强与传统方法相比,具有功能相似但结构不同的药物线索的识别.
主要方法:
- 利用化学语言模型进行基于矢量的化学相似性搜索.
- 实施了一个快速的工程策略,用于查询和数据库的独特化学字符串表示算法.
- 使用大语言模型 (LLM) 辅助的专利总结管道验证的结果.
主要成果:
- 基于矢量的搜索成功识别了具有与查询分子相似的专利衍生功能的分子.
- 鉴定到的分子中有很大一部分具有不同的结构和支架,传统的相似性搜索将会错过这些结构和支架.
- 该方法在9个不同的查询中显示出有效性.
结论:
- 开发的化学语言模型方法为药物发现提供了一个新的工具.
- 这种方法扩大了识别具有目标功能的新分子类的潜力.
- 它克服了传统基于结构的相似性搜索在发现结构上不同的线索方面的局限性.
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