Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance01:07

Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance

26
Drug transporters are critical in drug absorption, distribution, and excretion processes. They should be included in physiological-based pharmacokinetic (PBPK) models, which help predict human drug disposition. However, predicting this is challenging during drug development, especially when liver transport is involved. However, with a realistic representation of body transport processes, an accurate model may be possible.
A recent model describes pravastatin's hepatobiliary excretion,...
26
Pore Transport and Ion-Pair Transport01:17

Pore Transport and Ion-Pair Transport

335
Pore transport and ion-pair formation are critical mechanisms for the absorption and distribution of drugs in the body.
Pore transport, also known as convective transport, is a process where small molecules like urea, water, and sugars rapidly cross cell membranes as though there were channels or pores in the membrane. Although direct microscopic evidence is limited  but the concept of pores or channels is widely accepted based on physiological evidence. Despite the lack of direct...
335
Nonlinear Pharmacokinetics: Role of Transporters01:27

Nonlinear Pharmacokinetics: Role of Transporters

27
A drug's nonlinear kinetics can be influenced by a diverse range of transporter proteins that serve as crucial players in drug distribution. These transporters, found within cells, can enhance or reduce local drug concentrations by facilitating the influx or efflux of drugs. For instance, the expression of xenobiotic transporters can be influenced by factors such as age and gender, potentially impacting the linearity of drug response.
Polymorphisms occurring in drug transporters can alter...
27
Carrier-Mediated Transport01:06

Carrier-Mediated Transport

239
Carrier-mediated transport is a pivotal process in drug absorption, particularly for lipid-insoluble drugs, and encompasses facilitated diffusion and active transport. Facilitated diffusion allows drugs to move along their concentration gradient without energy expenditure, while active transport utilizes ATP to drive drug movement against this gradient.
Active transport involves two types of membrane-spanning transporters: uptake and efflux. Uptake transporters are expressed in the small...
239
Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs01:21

Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs

946
The fundamental mathematical principles, such as calculus and graphs, play crucial roles in analyzing drug movement and determining pharmacokinetic parameters. Differential calculus examines rates of change and helps to determine the dissolution rate of drugs in biofluids, as well as how drug concentrations change over time. For instance, it can help calculate the rate of elimination of a drug from the body based on its concentration-time profile.
On the other hand, integral calculus focuses on...
946
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

56
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
56

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Contrastive Pre-Training and Multiple Instance Learning for Predicting Tumor Microsatellite Instability.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Optimal Transport-Based Network Alignment: Graph Classification of Small Molecule Structure-Activity Relationships in Biology.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Structural Variant Prediction in Extended Pedigrees Through Sparse Negative Binomial Genome Signal Recovery.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2018
查看所有相关文章

相关实验视频

Updated: May 29, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.6K

基于最佳运输的图核用于药物财产预测.

Mohammed Aburidi1, Roummel Marcia1

  • 1Department of Applied MathematicsUniversity of California Merced Merced CA 95348 USA.

IEEE open journal of engineering in medicine and biology
|February 5, 2025
PubMed
概括

本研究介绍了基于最佳运输 (OT) 的图核,用于预测药物吸收,分布,新陈代谢,分泌和毒性 (ADMET) 特性. 这些新的方法优于当前的图形深度学习模型,在药物开发中提供了更好的准确性和可解释性.

科学领域:

  • 计算化学是一种计算化学.
  • 机器学习是机器学习.
  • 药物发现 药物发现

背景情况:

  • 优化制药剂特性 (ADMET) 是至关重要的,但由于实验成本和数据限制,具有挑战性.
  • 计算和预测工具,包括机器学习和基于图形的方法,在早期药物开发中变得越来越重要.
  • 现有的方法在准确有效地预测复杂的ADMET配置文件方面面临挑战.

研究的目的:

  • 开发和评估基于最佳传输 (OT) 理论的新型图核,用于预测药物ADMET特性.
  • 评估基于OT的图形内核与最先进的深度学习模型的性能.
  • 突出拟议方法的解释性,适应性和通用性的优点.

主要方法:

  • 利用最佳运输 (OT) 理论来构建三个图核.
  • 使用图形匹配生成相似性矩阵.
  • 将相似性矩阵集成到ADMET属性的预测建模框架中.

主要成果:

  • 基于OT的图形内核在19个ADMET数据集中表现出卓越的性能.
  • 在19个数据集中的9个中表现优于最先进的图形深度学习模型.
  • 在2个额外的数据集中显示了竞争性结果,在某些情况下甚至超过了高级图形神经网络 (GNN).
关键词:
在ADMET中,ADMET的属性是:最佳的运输类型图形内核 图形内核是指图形的核心.图表匹配对应的图表沃斯斯坦距离的距离

更多相关视频

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

2.3K
Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia
11:06

Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia

Published on: April 7, 2023

1.9K

相关实验视频

Last Updated: May 29, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.6K
Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

2.3K
Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia
11:06

Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia

Published on: April 7, 2023

1.9K

结论:

  • 基于OT的新型图核为ADMET属性预测提供了高效和竞争力的方法.
  • 这些方法比传统的图形神经网络提供了优势,包括增强的解释性,适应性和通用性.
  • 这项研究证实了OT理论在推进计算药物发现和开发方面的潜力.