Assay2Mol:基于大型语言模型的药物设计,使用生物测试环境
Yifan Deng1,2, Spencer S Ericksen3, Anthony Gitter1,2,4
1Department of Computer Sciences, University of Wisconsin-Madison.
ArXiv
|July 25, 2025
概括
Assay2Mol是一个新的工作流程,使用大型语言模型来分析生化查试验. 它通过利用现有测试数据来识别潜在的药物分子,用于新药发现.
科学领域:
- 生物化学 生物化学
- 计算机化药物发现技术
- 生物信息学是一种生物信息学.
背景情况:
- 科学数据库包含大量的定量和文本数据,对药物发现至关重要.
- 生物化学查试验评估分子对疾病点的反应.
- 测试数据中的非结构化文本在很大程度上未被用于药物发现.
研究的目的:
- 为了介绍Assay2Mol,一个基于大型语言模型的工作流.
- 利用现有的生物化学查试验用于早期药物发现.
- 为了克服测试数据中非结构化文本的局限性.
主要方法:
- Assay2Mol检索与新目标相似的目标的现有测试记录.
- 它采用了与检索查数据的上下文学习.
- 根据测试信息生成候选分子.
主要成果:
- Assay2Mol的性能优于最近的机器学习方法,用于生成候选配体分子.
- 工作流促进生成更容易合成的分子.
- 有效地利用现有的生化查试验数据.
结论:
- Assay2Mol为早期药物发现提供了一种新的方法.
- 使用LLM利用非结构化试验数据可以加快对候选药物的识别.
- 该方法增强了用于药物发现活动的分子生成.
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