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相关概念视频

Drug-Receptor Interaction: Agonist01:25

Drug-Receptor Interaction: Agonist

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Agonists are drugs that interact with specific receptors in the body to produce a biological response. When an agonist binds to a receptor, it activates or enhances the receptor's function, leading to physiological effects. The interaction between agonist drugs and receptors is crucial for their therapeutic action in various medical treatments.
Agonists can bind to receptors in different ways. Some agonists bind directly to the receptor's active site, mimicking the endogenous...
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Drug-Receptor Interactions01:29

Drug-Receptor Interactions

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Drug-receptor interaction describes the binding of receptors by drugs, but not all drug-receptor interactions result in activation and tissue response. For instance, the binding of agonists activates the receptor to generate a cellular reaction, while antagonists bind to receptors without causing their activation.
Several parameters, such as the drug's affinity for its receptor and its efficacy, which is its ability to activate the receptor, determine the drug's effect on the tissue....
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Drug Discovery: Overview01:26

Drug Discovery: Overview

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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Drug Biotransformation: Overview01:16

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Pharmaceutical substances known as xenobiotics are predominantly lipophilic and nonionized. This enables them to permeate lipid bilayers, such as cell membranes, and interact with intracellular target receptors. Lipophilic drugs have an advantage in crossing biological barriers and reaching their intended sites of action. However, lipophilic drugs often have a restricted capacity for renal expulsion or elimination from the body. When these drugs enter the kidneys and undergo glomerular...
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Combined Effects of Drugs: Synergism01:27

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Synergism is a useful mechanism where combining two or more drugs is more effective than each constituent used alone. Such combinations are also called supra-additive interactions. The drugs collectively enhance the final therapeutic effect by acting on different targets. Another advantage is that the low dose of each constituent drug is sufficient to achieve the desired effect. This helps reduce the duration of therapy and lower the adverse effects of these drugs.
Such synergistic combinations...
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Drug-Receptor Interaction: Antagonist01:28

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An antagonist is a drug that binds strongly to a receptor without activating it. An antagonist prevents other molecules, such as neurotransmitters or hormones, from binding to the receptor and triggering a cellular response. Such interaction effectively hinders the normal physiological processes mediated by the receptor, resulting in various pharmacological effects depending on the specific receptor targeted.
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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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从生物医学文本中提取增强的药物相互作用,使用基于深度学习的句子表示.

Muhammad Talha Tahir1, Muhammad Ibrahim1, Nadeem Sarwar2

  • 1Department of Computer Science, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.

Scientific reports
|October 30, 2025
PubMed
概括

一个新的CNN-DDI模型有效地从生物医学文本中提取药物相互作用 (DDI). 这种模型实现了高精度,超越了传统和基于变压器的方法,同时需要更少的计算资源.

关键词:
药物不良反应 (ADR) 是一种药物不良反应.在BioBERT和CNN的架构中.生物医学自然语言处理 (NLP)进行比较分析.深度学习模型深度学习模型药物相互作用 (DDI) 是一种药物相互作用.基于变压器的模型

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科学领域:

  • 生物医学信息学 生物医学信息学
  • 自然语言处理自然语言处理.
  • 机器学习 机器学习

背景情况:

  • 药物相互作用 (DDI) 对患者安全构成重大风险,并增加医疗保健成本.
  • 传统的机器学习 (ML) 方法难以从生物医学文本中提取DDI的复杂性.
  • 先进的深度学习和变压器模型提供了更好的洞察力,但在计算上要求更高.

研究的目的:

  • 开发一种高效的卷积神经网络 (CNN) 模型,命名为CNN-DDI,用于从生物医学文献中提取DDI.
  • 将CNN-DDI的性能与传统的ML模型和基于最先进的变压器的模型进行比较.

主要方法:

  • 使用SemEval-2013数据集进行比较分析.
  • 对CNN-DDI与各种ML模型 (物流回归,SVM,随机森林,天真贝斯,决策树) 和变压器模型 (BioBERT,RoBERTa,DeBERTa,ELECTRA,DistilBERT) 的评估.
  • 标准化所有模型的参数调整和预处理程序.

主要成果:

  • CNN-DDI实现了最高的整体精度 (86.81%) 和F1得分 (83.81%).
  • CNN-DDI的表现优于基于变压器的模型 (最佳F1得分为81.41%) 和传统的ML模型 (最佳F1得分为77.09%).
  • 在显著减少计算要求的情况下,CNN-DDI表现出卓越的性能.

结论:

  • 在生物医学文本挖掘中,CNN-DDI为DDI提取提供了高效和计算效率的解决方案.
  • 该模型为大规模分析提供了一个可行的选择,平衡性能和资源需求.