分子设计的大型语言模型:弥合化学语法和生物语义之间的差距
Yingjun Chen1, Xinheng Guo2, Weiwei Xue2
1School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai 201219, China.
Drug discovery today
|February 23, 2026
概括
大型语言模型 (LLM) 通过实现自主分子设计来推动药物发现. 这次审查涵盖了LLM.
科学领域:
- 人工智能在药物发现中的作用
- 计算化学计算化学
- 生物信息学是一种生物信息学.
背景情况:
- 大型语言模型 (LLM) 越来越多地被用于早期药物发现.
- 传统的方法经常与化学语法和生物语义的复杂性作斗争.
- 弥合化学表征与生物功能之间的差距是一个关键的挑战.
研究的目的:
- 审查LLM在药物发现中的演变,从基于序列的模型到自主系统.
- 分析各种LLM范式,用于成功识别和领先优化.
- 讨论分子设计的结构意识和多式模式框架的进展.
主要方法:
- 对药物发现中的LLMs现有文献的审查.
- 分析各种LLM范式,包括新的设计和多目标优化.
- 探索结构意识,多式联络和自主代理框架.
主要成果:
- 法律法规正在从基于序列的模型演变为复杂的自主发现系统.
- 结构意识和多模式框架通过结合3D信息来增强分子设计.
- 自主代理可以编排闭环设计-制造-测试-分析 (DMTA) 周期.
结论:
- 在LLM驱动的药物发现中,计算预测和实验验证之间存在很大的差距.
- 未来的方向包括混合神经符号架构和统一的基础模型.
- 坚固和自主分子设计需要在LLM集成和验证方面取得进一步的进展.
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