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相关概念视频

Pharmacovigilance01:19

Pharmacovigilance

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Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...
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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Predicting Molecular Geometry02:27

Predicting Molecular Geometry

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VSEPR Theory for Determination of Electron Pair Geometries
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Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

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Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Ogive Graph01:07

Ogive Graph

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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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相关实验视频

Updated: Feb 8, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
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使用图形神经网络进行基于结构的QT延长风险预测:结合体内hERG试验和药监数据的综合方法.

Tomoyuki Enokiya1,2, Ryosuke Kunitomo1, Takamasa Yamaguchi2

  • 1Laboratory of Pharmacoinformatics, Department of Pharmaceutical Sciences, Faculty of Pharmaceutical Sciences, Suzuka University of Medical Science, Suzuka, Japan.

Clinical pharmacology and therapeutics
|February 7, 2026
PubMed
概括

我们开发了一个图形神经网络 (GNN),通过整合分子结构,体外数据和安全信号来预测药物诱导的QT间隔延长风险. 这种可解释的模型增强了药物安全性评估.

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科学领域:

  • 药理学 药理学是指药理学的学科.
  • 计算化学计算化学
  • 药品安全 药品安全

背景情况:

  • 药物诱导的QT间隔延长是药物开发中的一个主要安全问题,增加了Torsades de Pointes (TdP) 的风险.
  • 实验室hERG抑制试验是早期查的标准,但药监数据提供了对前节律失常风险的补充见解.
  • 将分子结构与多种数据源整合在一起,为预测药物心脏毒性提供了一种未被充分利用的方法.

研究的目的:

  • 开发一个可解释的图形神经网络 (GNN) 框架来预测QT责任.
  • 为了整合体外hERG抑制数据,FDA不良事件报告系统 (FAERS) 信号和分子结构信息.
  • 通过模型解释性技术,识别有助于QT责任的结构特征.

主要方法:

  • 使用 RDKit 开发了一个 GNN 框架,将 Canonical SMILES 转换为具有编码原子和键级特征的分子图形.
  • 对比了四个GNN架构 (GINE,GCN,GraphSAGE,GATv2) 使用分层的五倍交叉验证对4808个具有二进制QT风险标签的小分子进行了比较.
  • 利用集成梯度来解释表现最好的GATv2模型,并在独立的hERG测试数据集上验证性能.

主要成果:

  • 该GATv2模型实现了0.838的交叉验证ROC-AUC,0.830的PR-AUC和0.756.756的F1得分.
  • 在完整的数据集上进行重新训练,提高了ROC-AUC的0.918,PR-AUC的0.908,以及F1得分的0.847.
  • 外部验证显示,ROC-AUC为0.859,灵敏度为0.80,特异性为0.82;原子度和数是关键预测因素.

结论:

  • 开发的GNN框架有效地整合了结构和药理数据,以预测QT风险.
  • 该模型的可解释性为药物开发提供了一个透明的,基于结构的决策支持工具.
  • 这种方法与监管指南 (ICH S7B/E14) 和CIPA等加强药物安全评估的倡议保持一致.