人工智能大型语言模型在药物标发现中的应用
Xinyu Liu1, Jia Zhang1, Xiaoran Wang1
1Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Frontiers in pharmacology
|July 23, 2025
概括
人工智能 (AI) 大型语言模型 (LLM) 正通过高效分析复杂的生物数据来彻底改变药物向发现. 这些先进的AI模型加速了潜在药物点的识别,克服了传统方法的局限性.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 传统的药物向发现方法面临着重大的技术和资源挑战.
- 人工智能的进步,特别是大型语言模型 (LLM),提供了新的解决方案.
- 法律学可以处理和整合大量的生物文献和奥米克数据.
研究的目的:
- 审查最近应用LLM在药物标发现方面的进展.
- 突出LLM在基因组学,转录组学,蛋白质组学和多组学分析方面的能力.
- 讨论这个领域当前的挑战和未来的方向.
主要方法:
- 应用自然语言处理 (NLP) 和深度学习 (转换器架构) 来进行生物数据分析.
- 无监督的预培训 (例如,面具语言建模) 和监督的LLMs微调.
- 整合文献数据,疾病途径和多omics数据集 (基因组学,转录组学,蛋白质组学,单细胞).
主要成果:
- LLM提高了识别致病基因变异和预测基因表达的准确性.
- 通过LLM,可以全面重建基因调节网络和蛋白质分析.
- 单细胞多omicsLLMs促进跨技术数据集成,为目标发现提供多维证据.
结论:
- LLM显著提高了药物标识的效率和准确性.
- 这些人工智能驱动的方法克服了传统查方法的局限性.
- 未来的研究应该专注于解决现有的技术挑战,并探索在药物发现中新的LLM应用.
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