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

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

764
Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
764
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

103
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
103
Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

70
Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
70
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

94
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
94
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

72
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...
72
Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

4.5K
An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
4.5K

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

Quality by design-guided development of silica-enabled lipid hybrid nanoparticles for enhanced olaparib dissolution.

Pharmaceutical development and technology·2026
Same author

In situ nasal gel loaded with Lactoferrin-Coated Brexpiprazole nanostructured lipid carriers for Schizophrenia: Cross-Species validation in Ketamine-Induced rat and zebrafish models.

European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V·2026
Same author

Assessing the impact of data harmonization on human liver microsomal stability prediction model performance.

Results in chemistry·2026
Same author

Transforming animal study toxicology reports into structured, harmonized data using large language models.

Archives of toxicology·2026
Same author

Hyaluronic Acid-functionalized Hesperidin-loaded Solid Lipid Nanoparticles for Mitigating Oxidative Stress: A Potential Strategy for Radiation-induced Skin Injury.

Applied biochemistry and biotechnology·2026
Same author

Depression patient-friendly formulation containing escitalopram and ascorbic acid: design, optimization, characterization, and in vivo taste assessment.

Naunyn-Schmiedeberg's archives of pharmacology·2026

相关实验视频

Updated: Jul 17, 2025

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

2.6K

人工智能在ADME财产预测中的应用

Vishal B Siramshetty1,2, Xin Xu1, Pranav Shah3

  • 1National Center for Advancing Translational Sciences, Rockville, MD, USA.

Methods in molecular biology (Clifton, N.J.)
|September 7, 2023
PubMed
概括

包括机器学习和人工智能在内的计算方法越来越多地用于预测药物发现的吸收,分布,新陈代谢和分泌 (ADME) 特性. 这些先进的技术优化了候选药物.

关键词:
ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME深度神经网络 深度神经网络图形神经网络的神经网络机器学习 机器学习质量结构-活动关系

更多相关视频

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

1.9K
A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

68.7K

相关实验视频

Last Updated: Jul 17, 2025

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

2.6K
Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

1.9K
A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

68.7K

科学领域:

  • 药理动力学和药物发现
  • 计算化学和化学信息学
  • 机器学习和人工智能在药理学中的应用

背景情况:

  • 吸收,分布,新陈代谢和分泌 (ADME) 是影响药物的有效性和安全性的关键药理学特性.
  • 通过计算预测ADME属性对于有效的药物发现和开发至关重要.
  • 机器学习 (ML) 和人工智能 (AI) 正在成为药理动力学预测的强大工具.

研究的目的:

  • 审查最近计算方法的进展,以预测小分子的ADME特性.
  • 突出各种神经网络架构在计算机辅助药物设计中的应用.
  • 讨论这些预测模型对优化药物发现管道的影响.

主要方法:

  • 机器学习和人工智能算法的应用.
  • 使用多种神经网络架构,包括深度神经网络,循环神经网络,图形神经网络和变压器网络.
  • 专注于ADME属性的计算预测.

主要成果:

  • 对ML/AI在预测药理动力学特征方面表现出显著的兴趣和应用.
  • 神经网络架构已经彻底改变了计算机辅助药物设计.
  • 这些方法有助于优化化学库和优先考虑候选药物.

结论:

  • 基于ML/AI的ADME属性的预测代表了药物发现中的范式转变.
  • 先进的计算技术提高了识别和优化潜在药物分子的效率.
  • 该领域的持续发展有望加速提供更安全,更有效的治疗方法.