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Enzyme-linked receptors are cell-surface receptors acting as an enzyme or associating with an enzyme intracellularly. They make excellent drug targets. Drugs can bind to the extracellular ligand-binding domain or directly affect their enzymatic domain and alter their activity.
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Drug-receptor bonds are formed through various chemical forces when drugs interact with target cells. Covalent bonds, strong and irreversible, are exemplified by DNA-alkylating anticancer agents that inhibit cell division. However, such irreversible drug binding lacks selectivity and can modify the DNA of the surrounding healthy cells. Covalent binding often contributes to tissue toxicity, as seen with chloroform and paracetamol metabolites binding to the liver, causing hepatotoxicity.
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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Pharmaceutical equivalents, by definition, are drug products with the same active ingredient in the same quantities, encapsulated in identical dosage forms, and intended for the same administration routes. These pharmaceutical equivalents are deemed bioequivalent if the bioavailability of the active entity in the drug preparations is similar. Moreover, pharmaceutical equivalents demonstrating bioequivalence are also regarded as therapeutically equivalent. This means that when used as directed,...
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The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
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LEAP:LLM指令-示例适应性提示框架用于生物医学关系提取.

Huixue Zhou1, Mingchen Li2, Yongkang Xiao1

  • 1Institute for Health Informatics, University of Minnesota, Minneapolis, MN 55455, United States.

Journal of the American Medical Informatics Association : JAMIA
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概括

通过使用自适应提示,LEAP框架增强了用于生物医学关系提取的大型语言模型 (LLM). 这种方法比标准调提高了性能,特别是在复杂的数据提取任务中.

关键词:
生物医学关系提取提取指令调整调整的指令.指令-示例适应式提示提示.大型语言模型自然语言处理自然语言处理.

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科学领域:

  • 生物医学信息学是生物医学信息学.
  • 自然语言处理自然语言处理.
  • 人工智能的人工智能

背景情况:

  • 大型语言模型 (LLM) 对生物医学关系提取有希望.
  • 在LLMs当前的演示方法需要优化复杂的生物医学任务.
  • 需要适应性调整策略来提高LLM在专业领域的表现.

研究的目的:

  • 研究生物医学关系提取中的大型语言模型 (LLM) 的自适应调整方法.
  • 引入和评估LLM指令-示例自适应提示 (LEAP) 框架.
  • 评估适应性任务描述和示例对LLM绩效的影响.

主要方法:

  • 在生物医学数据任务中分析了LLM的演示组件 (任务描述,示例).
  • 开发和实施LEAP框架,使用三个适应性调整方法:指令,示例和指令-示例适应性调整.
  • 在使用Llama2和MedLLaMA等LLM对DDI,ChemProt和BioRED数据集进行LEAP评估.

主要成果:

  • 指令 + 选项 + 例子促使F1得分明显提高,与零射击LLMs的标准方法相比.
  • 在所有测试模型中,LEAP框架,特别是适应性提示的例子,在所有测试模型中都超过了传统的指令调整.
  • MedLLAMA_13B在ChemProt上获得了95.13的F1分,在DDI和BioRED数据集上表现出强的表现.

结论:

  • LEAP框架提供了一种有效的策略,以加强生物医学关系提取的LLM培训.
  • 这种方法倾向于动态,上下文丰富的提示,而不是广泛的微调.
  • 在复杂的数据提取场景中,LEAP为提高LLM能力提供了一个有希望的方向.