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

Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

1.0K
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...
1.0K
Drug-Receptor Interactions01:29

Drug-Receptor Interactions

5.4K
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....
5.4K
Drug Discovery: Overview01:26

Drug Discovery: Overview

8.1K
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...
8.1K
Targets for Drug Action: Overview01:26

Targets for Drug Action: Overview

6.4K
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...
6.4K
Factors Affecting Protein-Drug Binding: Drug Interactions01:23

Factors Affecting Protein-Drug Binding: Drug Interactions

202
Drug interactions are a critical aspect of pharmacology and can occur when two or more drugs compete for the same binding site. This competition can result in one drug displacing another, altering the effect of the displaced drug. Drug interactions are complex processes that rely heavily on how much of the displacer drug is present and how strongly it can bind to the same sites as the displaced drug.
Displacement interactions can have varying outcomes, ranging from toxicity to virtually...
202
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

789
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...
789

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

On the state of protein function prediction: a report on the fourth CAFA challenge.

bioRxiv : the preprint server for biology·2026
Same author

PDBe-SIFTS: an open-source tool for Structure Integration with Function, Taxonomy, and Sequences, featuring improved alignment, scoring scheme, and accelerated search.

bioRxiv : the preprint server for biology·2026
Same author

Advances in Protein Function Prediction from the Fifth CAFA Challenge.

bioRxiv : the preprint server for biology·2026
Same author

Upcycling pyrolyzed biowaste into functional alkali-activated biochar for hydrogen sulphide capture.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA·2026
Same author

An AI-Ready Phosphorylation Meta-Analysis for <i>Saccharomyces cerevisiae</i>.

Journal of proteome research·2026
Same author

A Landscape Analysis of Human SUMOylation.

Molecular & cellular proteomics : MCP·2026

相关实验视频

Updated: Jul 25, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

2.7K

转移学习用于药物向相互作用预测.

Alperen Dalkıran1,2, Ahmet Atakan1,3, Ahmet S Rifaioğlu4,5

  • 1Department of Computer Engineering, Middle East Technical University, Ankara 06800, Turkey.

Bioinformatics (Oxford, England)
|June 30, 2023
PubMed
概括

深度转移学习有效地预测药物向相互作用的研究不足的蛋白质与有限的数据. 这种人工智能方法在训练数据集小时优于传统方法,加速药物发现.

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

4.2K
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

18.7K

相关实验视频

Last Updated: Jul 25, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

2.7K
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

4.2K
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

18.7K

科学领域:

  • 计算生物学是一种计算生物学.
  • 人工智能在药物发现中的作用
  • 生物信息学是一种生物信息学.

背景情况:

  • 药物向相互作用 (DTI) 的预测对于药物发现至关重要.
  • 人工智能驱动的DTI预测方法需要大量的训练数据,这对于研究不足的蛋白质通常是不可用的.
  • 深度转移学习为使用有限数据进行DTI预测提供了一个潜在的解决方案.

研究的目的:

  • 调查深度转移学习对预测药物向相互作用 (DTI) 的有效性,涉及缺乏训练数据的研究不足的蛋白质.
  • 为了评估与从头开始训练深度神经网络相比转移学习的性能,用于DTI预测.

主要方法:

  • 一个深度神经网络分类器在一个大,通用的源数据集上进行了预训练.
  • 预先训练的网络使用较小的,专门的目标数据集进行了微调,用于研究不足的蛋白质家族 (例如,载体,核受体).
  • 在不同转移学习策略中系统评估绩效,并与从头开始的传统培训进行比较.

主要成果:

  • 当目标训练数据集包含不到100个化合物时,深度转移学习显著超过了从头开始的训练.
  • 这项研究表明,转移学习的优势是预测药物结合剂对未研究过的蛋白质标的作用.
  • 该方法在生物医学中使用关键蛋白家族进行了验证,包括激酶,GPCR,离子通道,核受体,蛋白酶和载体.

结论:

  • 深度转移学习是药物向相互作用预测的强大和有利的方法,特别是对于具有有限可用的培训数据的目标.
  • 这种方法可以加速对未经研究的蛋白质的候选药物的鉴定,解决药物发现的关键瓶.
  • 开发的模型和代码是公开的,这有助于进一步的研究和应用.