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

7.9K
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...
7.9K
Targets for Drug Action: Overview01:26

Targets for Drug Action: Overview

6.3K
Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
Receptors are either membrane-spanning or intracellular proteins, which upon binding a ligand, get activated and transmit the signal downstream to elicit a response. Drugs bind receptors, either mimicking the action of endogenous ligands or blocking the receptor activity to bring about a modified response. Nearly 35% of approved drugs target the G...
6.3K
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

720
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...
720
Combined Effects of Drugs: Synergism01:27

Combined Effects of Drugs: Synergism

3.9K
Synergism is a useful mechanism where combining two or more drugs is more effective than each constituent used alone. Such combinations are also called supra-additive interactions. The drugs collectively enhance the final therapeutic effect by acting on different targets. Another advantage is that the low dose of each constituent drug is sufficient to achieve the desired effect. This helps reduce the duration of therapy and lower the adverse effects of these drugs.
Such synergistic combinations...
3.9K
Drug-Receptor Interactions01:29

Drug-Receptor Interactions

5.2K
Drug-receptor interaction describes the binding of receptors by drugs, but not all drug-receptor interactions result in activation and tissue response. For instance, the binding of agonists activates the receptor to generate a cellular reaction, while antagonists bind to receptors without causing their activation.
Several parameters, such as the drug's affinity for its receptor and its efficacy, which is its ability to activate the receptor, determine the drug's effect on the tissue....
5.2K
Combined Effects of Drugs: Antagonism01:30

Combined Effects of Drugs: Antagonism

8.5K
The combined effects of drugs can result in various interactions, of which an important type is antagonism. Antagonism is a mechanism where one drug inhibits or counteracts the effects of another drug. Antagonism can occur through various means, including receptor binding, allosteric modulation, functional interaction, chemical reactions, and pharmacokinetic processes.
The most common type is receptor antagonism, where one drug acts as an antagonist to block the effects of another drug by...
8.5K

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

Uncovering hidden biomarkers in systemic lupus erythematosus through mqTrans analysis.

Computational biology and chemistry·2026
Same author

SeqHIVE: a Python package to convert the biological sequences to informative vectors for sequence property predictions.

BioData mining·2026
Same author

A deep neural network model for optimizing traditional Chinese medicine prescriptions with data augmentation.

British journal of pharmacology·2025
Same author

AutoFE-Pointer: Auto-weighted feature extractor based on pointer network for DNA methylation prediction.

International journal of biological macromolecules·2025
Same author

Definer: A computational method for accurate identification of RNA pseudouridine sites based on deep learning.

PloS one·2025
Same author

Channel attention convolutional aggregation network based on video-level features for EEG emotion recognition.

Cognitive neurodynamics·2024
Same journal

DeepDPM: A Deep Learning Method for MoRFs Prediction Based on Wavelet Transform and Dynamic Convolutional Attention Mechanism.

Journal of chemical information and modeling·2026
Same journal

Graph-Based Generation and Reduction of Complex Chemical Reaction Networks.

Journal of chemical information and modeling·2026
Same journal

Modeling the Sensitivity of Large-Scale Virtual Screening to Scoring Function Accuracy, Artifacts, and Library Composition.

Journal of chemical information and modeling·2026
Same journal

Machine Learning-Driven Discovery of Indole/Oxoindole-Piperazine Scaffolds as Dual MAO-B/Sig-1R Ligands for Neurodegenerative Disorders.

Journal of chemical information and modeling·2026
Same journal

Mapping Evolution of Molecules across Biochemistry with Assembly Theory.

Journal of chemical information and modeling·2026
Same journal

Structural Proteomics-Based Deciphering of Hydrophobic Packing Fingerprints Informing Protein Thermostability in TIM Barrels.

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

相关实验视频

Updated: Jul 2, 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

9.6K

MRNDR:药物重用多头注意力基础推网络.

Xin Feng1,2,3, Zhansen Ma4, Cuinan Yu5

  • 1School of Science, Jilin Institute of Chemical Technology, Jilin 130000, P.R. China.

Journal of chemical information and modeling
|February 19, 2024
PubMed
概括
此摘要是机器生成的。

药物重定向通过预测现有药物的新用途来加速新药的开发. 一个新的模型,MRNDR,使用多头注意力和一种新的算法来实现最先进的药物疾病预测,减少成本和时间.

更多相关视频

Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

18.6K
High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method
07:51

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

11.8K

相关实验视频

Last Updated: Jul 2, 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

9.6K
Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

18.6K
High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method
07:51

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

11.8K

科学领域:

  • 计算生物学是一种计算生物学.
  • 药理学 药理学 是一个学科.
  • 人工智能在药物发现中的作用

背景情况:

  • 药物开发是昂贵和耗时的.
  • 药物再利用提供了一个更有效的替代方案,但仍然需要进行广泛的疗效测试.
  • 预先选现有药物的潜在新适应症可以显著降低成本并加快这一过程.

研究的目的:

  • 引入一种新的药物重定向推模型,即MRNDR (药物重定向多头关注型推网络).
  • 开发一种药物疾病关系的预测工具,以提高药物重用效率.
  • 利用先进的人工智能技术,实现准确,经济高效的药物重定位.

主要方法:

  • 使用了一百万级培训数据集 (BioRE - 生物推实体数据).
  • 实施了多头自我注意机制,以实现强大的概括.
  • 采用了拟议的权重表示距离得分 (WRDS) 算法.

主要成果:

  • 在GP-KG公共数据集上实现了最先进的性能.
  • 获得了0.308的MRR (平均互惠等级) 和0.628.6的Hits@10得分.
  • 与现有模型相比,显示了4.7% (MRR) 和18.1% (Hits@10) 的显著改进.

结论:

  • 该MRNDR模型有效地预测了药物与疾病的关系,促进了有效的药物重新定位.
  • 通过临床试验数据的验证间接证实了MRNDR建议的实际适用性.
  • MRNDR减少了手动专家评估的需要,简化了药物重定向管道.