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

8.8K
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...
8.8K
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

155
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
155
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

1.1K
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...
1.1K
Factors Affecting Protein-Drug Binding: Drug Interactions01:23

Factors Affecting Protein-Drug Binding: Drug Interactions

292
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...
292
Protein-protein Interfaces02:04

Protein-protein Interfaces

13.8K
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
13.8K
Protein-Drug Binding: Mechanism and Kinetics01:16

Protein-Drug Binding: Mechanism and Kinetics

1.0K
Protein-drug binding refers to the interaction between drugs and proteins within the body. This binding process can occur intracellularly, involving drug interactions with enzymes or receptors within cells, or extracellularly, involving plasma proteins in the blood.
Various forces drive these interactions, including hydrogen bonds, hydrophobic interactions, ionic bonds, electrostatic interactions, and van der Waals forces. These bonds enable drugs to bind to specific sites on proteins,...
1.0K

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

Synthesis and characterization of silver nanowires and their promising anticancer effects.

Nanoscale horizons·2026
Same author

Exploring the therapeutic potential of <i>Tetrastigma bracteolatum</i> (Wall.) Planch. methanol extract and its different fractions: <i>in vitro, in vivo,</i> and <i>in silico</i> approaches.

Frontiers in pharmacology·2026
Same author

Trace detection of carbaryl pesticide in food samples via a highly efficient and novel g-C<sub>3</sub>N<sub>4</sub>/MXene-based electrochemical sensor.

Food chemistry·2026
Same author

Self-attention U-Net (SAU-Net): An attention-driven U-Net framework for precise brain tumor segmentation using multimodal magnetic resonance imaging.

Digital health·2026
Same author

Phytochemical Profiling and Computational Docking Studies Revealing the Potential Anticancer and Antioxidant Properties of <i>Heliotropium indicum</i> L.

Scientifica·2026
Same author

Retraction notice to "Growing crystals and studying structure and electronic properties of Cu<sub>2</sub>ZnGe(S<sub>х</sub>Se<sub>1-x</sub>)<sub>4</sub> compositions" [Heliyon 9 (2023) e22533].

Heliyon·2026
Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026
Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026
Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026
Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026
Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026
Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026
查看所有相关文章

相关实验视频

Updated: Sep 19, 2025

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

8.4K

使用机器学习预测药物向相互作用,并改进数据平衡和功能工程.

Md Alamin Talukder1, Mohsin Kazi2, Ammar Alazab3,4

  • 1Department of Computer Science and Engineering, International University of Business Agriculture and Technology, Dhaka, Bangladesh. alamin.cse@iubat.edu.

Scientific reports
|June 3, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种使用机器学习和深度学习的新型混合框架,以改善药物向相互作用预测. 该方法有效地解决了数据不平衡,并提高了计算药物发现的准确性.

关键词:
计算机化药物发现.数据不平衡的数据不平衡药物-目标药物相互作用生成性的对抗性网络.机器学习是机器学习.随机森林分类器是随机森林分类器.

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

18.8K
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

4.3K

相关实验视频

Last Updated: Sep 19, 2025

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

8.4K
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

18.8K
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

4.3K

科学领域:

  • 计算机化药物发现.
  • 生物信息学是一种生物信息学.
  • 机器学习 机器学习

背景情况:

  • 药物向相互作用 (DTI) 的预测至关重要,但由于数据不平衡和复杂的生物化学表征而受到挑战.
  • 准确的DTI预测加速了治疗开发和制药研究.

研究的目的:

  • 为增强药物向相互作用 (DTI) 预测开发一种新的混合框架.
  • 解决数据不平衡,提高DTI预测模型的准确性和灵敏性.

主要方法:

  • 开发了一个混合框架,将机器学习 (ML) 和深度学习 (DL) 结合起来.
  • 特性工程利用MACCS密钥用于药物结构和氨基酸/二化合物用于目标性质.
  • 使用生成对抗网络 (GAN) 来平衡不平衡的数据集,并使用随机森林分类器 (RFC) 来进行预测.

主要成果:

  • GAN+RFC模型在BindingDB-Kd,BindingDB-Ki和BindingDB-IC50数据集中表现出高性能.
  • 在BindingDB-Kd.上达到高达97.46%的准确性,97.49%的精度和99.42%的ROC-AUC.
  • 显著提高了灵敏度,并减少了由于GANs.导致的虚假阴性.

结论:

  • 拟议的基于GAN的混合框架显著提高了DTI预测的准确性和稳定性.
  • 这种方法通过有效处理数据不平衡和复杂特征,为计算药物发现设定了新的基准.
  • 该框架的可扩展性和通用性为治疗开发做出了重大贡献.