药物发现中的自然语言处理:通过人工智能弥合文本和治疗之间的差距
Christine Ann Withers1, Amina Mardiyyah Rufai2, Aravind Venkatesan2
1Chemical Biology Services, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK.
Expert opinion on drug discovery
|April 29, 2025
概括
人工智能 (AI) 和机器学习 (ML) 正在通过实现先进的自然语言处理 (NLP) 来提取数据来彻底改变药物发现. 选择正确的NLP方法对于克服大量科学数据的挑战至关重要.
科学领域:
- 生命科学 生命科学
- 药物发现 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 科学数据的指数增长为药物发现带来了机遇和挑战.
- 人工智能 (AI) 的进步显著提高了用于分析科学文本的自然语言处理 (NLP) 能力.
- 机器学习 (ML) 技术越来越多地被采用来创新和加速药物发现管道.
研究的目的:
- 提供适用于药物发现的NLP技术的概述.
- 探索NLP从命名实体识别 (NER) 到大型语言模型 (LLM) 的演变.
- 在药物发现的背景下讨论各种NLP方法的优缺点.
主要方法:
- 使用欧洲PMC,ResearchRabbit和SciSpace进行文献综述.
- 使用本体学和ML模型探索命名实体识别 (NER).
- 对生物数据管理的知识图 (KG) 的研究.
- 对复杂数据处理的大型语言模型 (LLM) 的分析.
主要成果:
- 由AI/ML支持的NLP提供了强大的工具,用于从科学文献中提取和分析数据.
- 不同的NLP方法,包括NER,KG和LLM,都有明显的优缺点.
- 选择NLP方法应根据具体的用例和可用数据量身定制.
结论:
- 人工智能驱动的NLP正在通过实现高效的数据分析来改变药物发现.
- 对研究人员来说,了解各种NLP技术的细微差别是必不可少的.
- 对NLP工具的战略选择是利用大数据用于制药创新的关键.
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