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

469
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...
469
Drug Discovery: Overview01:26

Drug Discovery: Overview

7.3K
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...
7.3K
Drug Nomenclature01:17

Drug Nomenclature

1.5K
During the development of a new pharmaceutical, the manufacturer initially assigns a code name to the drug. Once approved, the drug receives a United States Adopted Name (USAN)—a generic, nonproprietary designation. Upon being listed in the United States Pharmacopeia, this nonproprietary name becomes the drug's official name. Additionally, the manufacturer assigns a proprietary name or trademark, which serves as the brand name under which the drug is marketed. It is worth noting that...
1.5K
Drug Classes and Categories01:25

Drug Classes and Categories

1.9K
Drugs can be classified according to their chemical composition or their intended therapeutic application. For instance, anti-infective agents that possess the ability to eliminate pathogens or suppress their growth and reproduction can be grouped based on the organisms they target or their chemical structure. Furthermore, drugs can be divided into prescription, nonprescription, or controlled substances. Prescription medications, such as antibiotics, require oversight from a licensed healthcare...
1.9K
Drug Administration and Therapy Phases: Overview01:26

Drug Administration and Therapy Phases: Overview

397
Drugs, the chemical agents used in diagnosing, treating, or preventing diseases, undergo a four-phase process of development: pharmaceutic, pharmacokinetics, pharmacodynamics, and therapeutic.
The pharmaceutical phase focuses on leveraging the physicochemical properties of the drug to design and manufacture an effective product. Variants include orally administered tablets or capsules, topical creams or ointments, and parenteral-delivery solutions or emulsions.
The pharmacokinetic phase...
397
Principles of Drug Action01:24

Principles of Drug Action

5.7K
Drugs are chemical substances that modify biological responses by interacting with macromolecular targets such as receptors, ion channels, transporters, and enzymes. Pharmacodynamics describes the course of action of drugs leading to the physiological effect at a specific site in the body.
Drugs can be agonists or antagonists. Like the endogenous ligands, agonists always bind and activate the target to produce a cellular response. Agonist binding induces a conformational change which in turn...
5.7K

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 review on modification of piezoelectric materials for wastewater treatment: mechanisms, applications, and future perspectives.

Water science and technology : a journal of the International Association on Water Pollution Research·2026
Same author

Predictors of in-hospital mortality in patients with multiple wasp stings in southwest China.

Frontiers in toxicology·2026
Same author

Cancer-associated adipocytes confer CDK4/6 inhibitor resistance in ER+ breast cancer through an IL-6/STAT3/SREBF2 axis coupled with cholesterol metabolism and cell cycle reprogramming.

International journal of biological sciences·2026
Same author

The Caspase-1/GSDMD/PXN/VCAM-1 Cascade Mediates Cerebral Ischemia-Reperfusion Injury.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology·2026
Same author

Tail-Suspension Model of Simulated Microgravity-Induced Functional Dyspepsia in Rats: Behavioral, Motility, and Brain-Gut Peptide Alterations.

International journal of molecular sciences·2026
Same author

PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction.

Science advances·2026
Same journal

PFASGroups: An Open-Source Framework for Automated Identification, Structural Classification, and Prioritization of Per- and Polyfluoroalkyl Substances.

Journal of chemical information and modeling·2026
Same journal

DeepKbhb: Context-Aware Prediction of Human Lysine β-Hydroxybutyrylation Sites.

Journal of chemical information and modeling·2026
Same journal

HyperDC: A Non-Uniform Hypergraph Framework for Dual- and Higher-Order Drug Combination Recommendation Across Diverse Complex Diseases.

Journal of chemical information and modeling·2026
Same journal

Correction to "AstraMEV (AI-Guided Structural Assembly of Multi-Epitope Vaccines) Against Infectious Bronchitis Virus".

Journal of chemical information and modeling·2026
Same journal

MolPy: A Large Language Model-Friendly Toolkit for Reactive Topology Editing in Polymer Simulations.

Journal of chemical information and modeling·2026
Same journal

Molecular Mechanisms of KIT Receptor Dimerization and Oncogenic Activation Revealed by Multiscale Simulations.

Journal of chemical information and modeling·2026
查看所有相关文章

相关实验视频

Updated: May 20, 2025

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

9.2K

基于知识的人工智能系统用于药物优先排序.

Yinchun Su1, Jiashuo Wu2, Xilong Zhao2

  • 1Department of Neurobiology, Harbin Medical University, Heilongjiang Provincial Key Laboratory of Neurobiology, Harbin 150081, China.

Journal of chemical information and modeling
|March 26, 2025
PubMed
概括
此摘要是机器生成的。

这项研究介绍了迷宫,这是一个新的AI框架,用于更快地发现药物. 它模拟人类的推理,以超过90%的准确性优先考虑候选药物,加速治疗开发.

更多相关视频

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.1K
Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

4.9K

相关实验视频

Last Updated: May 20, 2025

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

9.2K
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.1K
Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

4.9K

科学领域:

  • 计算生物学是一种计算生物学.
  • 生物信息学是一种生物信息学.
  • 药物发现 药物发现

背景情况:

  • 传统的药物发现是耗时和昂贵的.
  • 人工智能 (AI) 正在彻底改变药物开发.
  • 在 silico 方法提供了更快,更具成本效益的替代方案.

研究的目的:

  • 介绍一个新的计算框架,迷宫,用于药物优先级.
  • 模拟人类知识检索和推断,以识别潜在的药物候选者.
  • 提供一个R包,以便方便使用迷宫框架.

主要方法:

  • 综合临床试验数据,文献共发生情况,药物向相互作用和疾病相似性.
  • 开发了一种旨在模仿人类推理的计算框架.
  • 通过TCGA队列验证了20个疾病类别的框架.

主要成果:

  • 在临床试验阶段实现了超过90%的预测准确度.
  • 在TCGA队列中与临床实践有很强的一致性.
  • 展示了强大的ROC-AUC指标,平衡预测准确性和可解释性.

结论:

  • 迷宫框架有效地优先考虑了各种疾病的候选药物.
  • 该研究强调了将计算模型与人类推理对齐的重要性.
  • 迷宫R包可在GitHub上获得,以获得更广泛的应用.