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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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When it comes to infants and young children, they are typically administered smaller doses of medication in comparison to adults. This is primarily because their organ functions still need to fully develop, meaning their bodies are not as efficient at metabolizing or eliminating drugs. Additionally, their blood-brain barrier is more permeable than in adults. As a result, high concentrations of drugs can easily penetrate the central nervous system (CNS), potentially leading to neurological...
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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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Agonists can bind with and activate receptors, resulting in the formation of drug-receptor complexes. Once formed, these complexes catalyze many biochemical processes at the cellular level and subsequently induce a pharmacologic response. The degree of response is directly proportional to the fraction of activated receptors, which in turn, depends on the concentration of the drug at the receptor site as well as the sensitivity of the receptor. An increase in the administered dose contributes to...
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Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
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Updated: Jun 5, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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DRGAT:通过基于扩散的图表注意力网络预测药物反应.

Emre Sefer1

  • 1Artificial Intelligence and Data Engineering Department, Ozyegin University, Istanbul, Turkey.

Journal of computational biology : a journal of computational molecular cell biology
|December 6, 2024
PubMed
概括
此摘要是机器生成的。

我们开发了一种使用基因组数据预测药物反应的新方法. 我们的方法通过增加基因表达数据来提高预测准确性,从而增强个性化医疗.

关键词:
深度学习是一种深度学习.扩散扩散是一种扩散.发现药物的发现.药物反应预测 药物反应预测图表神经网络的神经网络

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科学领域:

  • 计算生物学是一种计算生物学.
  • 基因组学就是基因组学.
  • 机器学习是机器学习.

背景情况:

  • 个性化医疗依赖于从患者基因组资料中准确预测药物反应.
  • 深度学习,特别是图形神经网络,显示出希望,但面临的挑战是高维,小样本的omics数据,导致过拟合和糟糕的概括.
  • 基因表达 (GE) 数据的复杂性和基因间的关系进一步加剧了预测建模的问题.

研究的目的:

  • 引入一种新的药物反应预测方法,即药物反应图表注意力网络 (DRGAT).
  • 通过整合数据增强和高级图形神经网络,解决omics数据的挑战,包括过度拟合和糟糕的泛化.
  • 提高基于基因组信息预测患者药物反应的准确性和可靠性.

主要方法:

  • DRGAT将数据增强的无声扩散隐性模型与具有高阶邻近传播 (HO-GATs) 的图形注意网络 (GAT) 结合起来.
  • 无阴性扩散模型增强了有限和高维基因表达数据.
  • HO-GAT 捕捉复杂的基因间关系,并提高预测性能.

主要成果:

  • 与多种药物中最先进的模型相比,DRGAT方法在接收器操作特征曲线下的区域中实现了近5%的改善.
  • 这些结果证明了该方法增强的概括能力.
  • 实验验证了基于扩散的生成模型在增强omics数据和减轻其固有的局限性方面的有效性.

结论:

  • 在药物反应预测准确性和概括性方面,DRGAT提供了显著的进步.
  • 扩散模型显示出克服数据稀缺性和复杂性的巨大潜力.
  • 开发的方法有助于个性化医学的进步,通过使更可靠的基因组导向治疗决策成为可能.