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

Drug Discovery: Overview01:26

Drug Discovery: Overview

10.9K
Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
10.9K
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

1.6K
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...
1.6K
Dose-Response Relationship: Overview01:03

Dose-Response Relationship: Overview

4.7K
Agonists can bind with and activate receptors, resulting in the formation of drug-receptor complexes. Once formed, these complexes catalyze many biochemical processes at the cellular level and subsequently induce a pharmacologic response. The degree of response is directly proportional to the fraction of activated receptors, which in turn, depends on the concentration of the drug at the receptor site as well as the sensitivity of the receptor. An increase in the administered dose contributes to...
4.7K
Factors Affecting Drug Response: Overview01:21

Factors Affecting Drug Response: Overview

2.8K
When it comes to infants and young children, they are typically administered smaller doses of medication in comparison to adults. This is primarily because their organ functions still need to fully develop, meaning their bodies are not as efficient at metabolizing or eliminating drugs. Additionally, their blood-brain barrier is more permeable than in adults. As a result, high concentrations of drugs can easily penetrate the central nervous system (CNS), potentially leading to neurological...
2.8K
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

653
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
653
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

223
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...
223

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

A dataset of small protein conformational ensembles from all-atom molecular dynamics simulations.

Scientific data·2026
Same author

Inverse Association Between Composite Dietary Antioxidant Index and Prevalence of Pelvic Inflammatory Disease Among Women: A Cross-Sectional Study of NHANES 2013-2018.

Healthcare (Basel, Switzerland)·2026
Same author

ToxiSpecies: Task-Aware Meta-Learning for Cross-Species Modeling of Acute Chemical Toxicity under Distribution Shift.

Journal of chemical information and modeling·2026
Same author

A multimodal dataset and predictive model for the treatment of uterine fibroids with focused ultrasound ablation surgery.

Scientific data·2026
Same author

An epithelial cell fate-driven predictive model for liver metastasis risk in primary colorectal cancer through single-cell and multi-omics integration.

Journal of translational medicine·2026
Same author

Generative pretraining for drug molecule design with bidirectional structure-property optimization.

Communications chemistry·2026

相关实验视频

Updated: Jan 8, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K

SHIFT-DRP:用于药物反应预测的动态多规模主动学习.

Xintao Wang1, Huiyan Xu1, Yanpeng Zhao1

  • 1School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China.

Journal of chemical information and modeling
|December 15, 2025
PubMed
概括

这项研究介绍了SHIFT-DRP,这是用于药物反应预测的积极学习框架. 它有效地选择实验,提高模型准确性,减少对个性化癌症治疗的资源需求.

更多相关视频

An Organotypic High Throughput System for Characterization of Drug Sensitivity of Primary Multiple Myeloma Cells
09:41

An Organotypic High Throughput System for Characterization of Drug Sensitivity of Primary Multiple Myeloma Cells

Published on: July 15, 2015

9.0K
Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

1.1K

相关实验视频

Last Updated: Jan 8, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K
An Organotypic High Throughput System for Characterization of Drug Sensitivity of Primary Multiple Myeloma Cells
09:41

An Organotypic High Throughput System for Characterization of Drug Sensitivity of Primary Multiple Myeloma Cells

Published on: July 15, 2015

9.0K
Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

1.1K

科学领域:

  • 计算生物学是一种计算生物学.
  • 药物基因组学 药物基因组学
  • 机器学习在药物发现中的作用

背景情况:

  • 针对癌症药物反应预测的深度学习模型面临新型药物细胞系组合的挑战,因为化学空间覆盖范围有限.
  • 综合性实验查是不切实际的,并且统一的抽样对于药物-细胞系对的选择是无效的.

研究的目的:

  • 开发SHIFT-DRP,一种主动学习框架,用于智能选择药物细胞系对进行实验验证.
  • 在资源限制下最大限度地提高药物反应预测的模型改进.

主要方法:

  • 采用动态抽样策略,从多样性探索向不确定性改进过渡.
  • 使用预训练模型进行分子表示,以及用于药物细胞系相互作用的交叉注意力机制.

主要成果:

  • 在预测性能方面,SHIFT-DRP优于现有的积极学习方法.
  • 与统一采样相比,实验资源减少了24%.
  • 证明识别具有不同反应的结构相似化合物的能力.

结论:

  • SHIFT-DRP提供了一个有效的解决方案,用于指导实验查和数据收集,用于药物反应预测.
  • 对于推进精准医学发展具有重大意义.