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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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使用教师-学生大型语言模型的指令多约束分子生成.

Peng Zhou1,2, Jianmin Wang3, Chunyan Li4

  • 1College of Information Science and Engineering, Hunan University, Changsha, 410082, Hunan, China.

BMC biology
|April 24, 2025
PubMed
概括

一个新的大型语言模型,TSMMG,使用自然语言提示生成满足多个属性要求的分子. 这种先进的模型实现了高的有效性和成功率,有助于药物发现.

关键词:
大型语言模型.分子生成分子生成多重约束多重约束

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科学领域:

  • 计算化学是一种计算化学.
  • 人工智能在药物发现中的作用

背景情况:

  • 产生具有特定结构和特性的分子是计算化学的一个重大挑战.
  • 现有的模型和工具在全面的分子生成中往往不足.

研究的目的:

  • 开发一种先进的大型语言模型,用于多约束分子生成.
  • 为了使基于对所需性质的自然语言描述创建新型分子.

主要方法:

  • 引入了TSMMG (基于文本的结构和多约束生成) 模型.
  • 训练有素的TSMMG使用来自专家知识 ("教师") 的大量文本-分子对数据集.
  • 利用自然语言提示指导分子生成.

主要成果:

  • 在任务中,TSMMG实现了99%以上的分子有效性.
  • 证明了高的成功率:82.58% (2-限制),68.03% (3-限制),和67.48% (4-限制).
  • 展示了对新的属性组合和各种语言风格的零射击适应性.

结论:

  • TSMMG是通过自然语言实现多约束分子生成的有效框架.
  • 该模型对药物发现和相关科学领域有重大影响.