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基于Omics的大型语言模型:药物发现创新的新引擎

Xia Sheng^1,2, Xiaoya Zhang^1,2, Yuxin Xing^1,2,3

¹Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201103, China.

Acta pharmaceutica Sinica. B

|January 26, 2026

概括

大型语言模型 (LLM) 通过分析复杂的omics数据来识别疾病机制和治疗目标,提高效率和成功率来彻底改变药物发现.

科学领域:

生物医学信息学生物医学信息学
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:

由于疾病的复杂性,传统的药物发现面临低效率和高消耗.
奥米克技术 (基因组学,转录组学,蛋白质组学) 提供系统层面的洞察力,但也带来了数据挑战,如高维度和噪声.
大型语言模型 (LLM) 通过捕获复杂的模式和推断信息来解决这些数据挑战.

研究的目的:

提出一个框架,用于在药物发现中将LLMs与omics数据集成在一起.
分析LLM在处理omics数据挑战方面的能力.
详细介绍LLM应用程序,以发现疾病机制,识别药物点,预测药物反应和模拟细胞行为.

主要方法:

分析omics数据的LLM架构和学习范式.
在关键药物发现阶段详细介绍LLM应用.
讨论LLM驱动的洞察力,用于途径向药物开发.

主要成果:

实际上,LLM能够有效地处理omics数据集中固有的复杂性和噪音.
士学位的应用涵盖疾病机制阐明,目标识别和药物反应预测.
整合Omics的LLMs促进了向以途径为中心的药物开发策略的转变.

结论:

通过利用omics数据,LLM提供了一种强大的方法来克服传统药物发现的局限性.
该框架为利用LLM在OMIC驱动的药物发现和开发中提供了指导.
将LLM与omics数据集成,可以实现更有效,更有生物学依据的治疗策略.

关键词:

药物发现药物发现一般化一般化一般化大型语言模型.欧米克斯集成技术的整合扰乱模型的模拟.代表性的学习学习.单细胞机是一种单细胞机.目标识别目标识别

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