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

Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

489
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
489
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

33.7K
VSEPR Theory for Determination of Electron Pair Geometries
33.7K
What is Metabolism?00:52

What is Metabolism?

112.2K
Overview
112.2K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

41
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...
41
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

12
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...
12
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

39
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...
39

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

Bundle care interventions for patients with perianal pain: A scoping review.

Medicine·2026
Same author

Hypoxia and lactate metabolism-related gene COL5A3 promotes triple negative breast cancer progression via DDR1/FAK/PI3K/AKT pathway.

Biology direct·2026
Same author

Genes From Epithelial-Mesenchymal Transition Predict Overall Survival Effectively in Breast Cancer: A Novel Risk Model Based on Initial Step of Tumor Metastasis.

Breast cancer : basic and clinical research·2026
Same author

M2 macrophages predict response to neoadjuvant chemotherapy in triple negative breast cancer patients.

Scientific reports·2026
Same author

Recognition of immunogenomic signature and prognostic value of the subtype of epithelial-mesenchymal transition in breast cancer.

Biochemistry and biophysics reports·2026
Same author

Gas-Phase F-Atom Migration Reactions of Perfluoroalkyl and Polyfluoroalkyl Sulfonic/Sulfinic Anions.

Rapid communications in mass spectrometry : RCM·2026

相关实验视频

Updated: May 7, 2025

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.2K

MTGGF:一种代谢类型感知图形生成模型,用于分子代谢物预测.

Peng-Cheng Zhao1, Xue-Xin Wei1, Qiong Wang1

  • 1School of Life Sciences, Northwestern Polytechnical University, Xi'an, 710072, China.

Interdisciplinary sciences, computational life sciences
|January 6, 2025
PubMed
概括

本研究引入了一种新的图形生成框架 (MTGGF),用于预测药物代谢物,提高准确性和可解释性,而不是现有的计算方法,以实现更安全的药物开发.

关键词:
注意力 注意力 注意力 注意力微调的微调方式图形生成模型的图形生成模型.分子代谢的分子代谢.准备培训 准备培训

更多相关视频

A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

20.9K
Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis
11:25

Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis

Published on: July 11, 2014

32.5K

相关实验视频

Last Updated: May 7, 2025

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.2K
A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

20.9K
Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis
11:25

Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis

Published on: July 11, 2014

32.5K

科学领域:

  • 计算化学是一种计算化学.
  • 药物代谢药物代谢
  • 机器学习在药物发现中的作用

背景情况:

  • 药物的体内代谢产生代谢物,这在药物开发中带来了安全挑战.
  • 实验性地确定代谢物是昂贵且耗时的.
  • 目前的基于规则和无规则的计算方法在预测新型代谢反应和表征分子结构方面存在局限性.

研究的目的:

  • 为准确的分子代谢物预测提出一种新的代谢类型感知图形生成框架 (MTGGF).
  • 解决现有的无规则方法在结构性特征和可解释性方面的局限性.
  • 加强药物开发中药物代谢物的风险评估.

主要方法:

  • 开发了一种两阶段的学习过程:对一般化学反应进行预训练和对特定类型的代谢反应进行微调.
  • 采用了一种复杂的图形对图形生成模型,将分子视为两部分图 (原子和键作为顶点).
  • 集成的交互式注意力机制,用于分析分子-代谢物关系.

主要成果:

  • 与最先进的方法相比,MTGGF框架在代谢物预测方面表现优越.
  • 废除研究验证了图形编码组件和特定类型微调的有效性.
  • 案例研究揭示了批准药物中的代谢类型特定的关键子结构.

结论:

  • 农业农产品和农业农业发展基金框架为预测分子代谢物提供了一种可靠和可解释的方法.
  • 已识别的代谢类型特定的子结构可以帮助预测潜在的安全问题.
  • 这一框架有可能在药物研究中显著改善药物代谢物的风险评估.