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

721
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...
721
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

41
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
41
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

982
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...
982
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

127
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
127
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

62
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...
62
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

84
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
84

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

The Mechanism of Acid-Catalyzed Decarboxylation of Aromatic <i>o</i>-Hydroxycarboxylic Acids: Insights from <i>o</i>-Hydroxynaphthoic Acids.

The Journal of organic chemistry·2026
Same author

Is segmentation solved? An evaluation of vision foundation models for head and neck tumor segmentation.

Physics in medicine and biology·2026
Same author

MassSeg-Framework: A Breast Mass Detection and Segmentation Framework Based on Deep Learning and an Active Contour Model.

Life (Basel, Switzerland)·2026
Same author

Computational Identification of Potential Novel Allosteric IHF Inhibitors Using QSAR Modeling to Inhibit Plasmid-Mediated Antibiotic Resistance.

International journal of molecular sciences·2026
Same author

In silico discovery of thioglycoside analogues as donor-site inhibitors of glycosyltransferase LgtC.

Scientific reports·2026
Same author

A 2026 Update on Computational Approaches to the Discovery and Design of Antimicrobial Peptides.

Antibiotics (Basel, Switzerland)·2026

相关实验视频

Updated: Jul 3, 2025

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
00:05

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

13.9K

在QSAR模型中重新思考适用性领域分析.

Jose R Mora1, Edgar A Marquez2,3, Noel Pérez-Pérez4

  • 1Departamento de Ingeniería Química, Universidad San Francisco de Quito (USFQ), Instituto de Simulación Computacional (ISC- USFQ), Diego de Robles y Vía Interoceánica, Quito, 170901, Ecuador.

Journal of computer-aided molecular design
|February 13, 2024
PubMed
概括

量化结构-活动关系 (QSAR) 模型经常高估可靠性,因为具有挑战性的适用性域 (AD) 估计. 错误分析显示,不可靠的预测聚集在错误较高的子空间中,需要更严格的AD指南和验证.

关键词:
适用性领域 适用性领域错误分析 错误分析经合组织原则 经合组织原则在QSAR中使用QSAR.

更多相关视频

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

1.1K
Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
16:02

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

2.7K

相关实验视频

Last Updated: Jul 3, 2025

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
00:05

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

13.9K
Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

1.1K
Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
16:02

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

2.7K

科学领域:

  • 计算化学是一种计算化学.
  • 毒理学 毒理学 毒理学
  • 药物发现 药物发现

背景情况:

  • 量化结构-活动关系 (QSAR) 模型被广泛使用,但通常会产生过于乐观的预测.
  • 适用性域 (AD) 的估计是QSAR建模的关键但具有挑战性的方面,影响预测可靠性.

研究的目的:

  • 调查QSAR模型预测和适用性域 (AD) 估计的可靠性.
  • 为AD分析和模型改进策略提出改进建议.

主要方法:

  • 将基于树的错误分析工作流应用于来自QsarDB存储库的五个QSAR模型.
  • 分析了雄激素受体生物活性和膜透性的模型.

主要成果:

  • AD预测错误主要发生在预测错误率最高的子空间中,这表明AD空间不均.
  • 证明不可靠的预测不是随机分布的,而是集中在特定的数据子集中.

结论:

  • 要求更严格的AD分析指南,包括模型错误分析.
  • 提倡对特定模型空间的AD方法进行严格验证.
  • 突出了错误分析对于合理的QSAR模型改进和数据扩展的有用性.