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

The Blood-brain Barrier00:49

The Blood-brain Barrier

52.1K
Overview
52.1K
Physiological Barriers01:25

Physiological Barriers

5.0K
Physiological barriers are semi-permeable cellular structures restricting drug diffusion into intracellular compartments and tissues. There are six types of physiological barriers: blood endothelial, cell membrane, blood-brain, blood-cerebrospinal fluid (CSF), blood-placenta, and blood-testis barriers.
The blood endothelial barrier is the most porous of these. It allows all small ionized, un-ionized, and lipophilic molecules to pass through the endothelial lining into the interstitial space...
5.0K

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

Failure Modes of Time Series Interpretability Algorithms for Critical Care Applications and Potential Solutions.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same author

PHEONA: An Evaluation Framework for Large Language Model-based Approaches to Computational Phenotyping.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same author

SHREC: A framework for advancing next-generation computational phenotyping with large language models.

PLOS digital health·2026
Same author

Comparative Evaluation of USG, CT, and MRI in Acute Pancreatitis.

Journal of pharmacy & bioallied sciences·2026
Same author

Graph-spa: A Spatiotemporal Graph Neural Network based framework for ARDS prediction and interpretability.

Journal of biomedical informatics·2025
Same author

Deciphering context-specific Axitinib escape pathways via multi-omics and explainable machine learning.

Journal of translational medicine·2025

相关实验视频

Updated: Jan 7, 2026

An In Vivo Blood-brain Barrier Permeability Assay in Mice Using Fluorescently Labeled Tracers
09:35

An In Vivo Blood-brain Barrier Permeability Assay in Mice Using Fluorescently Labeled Tracers

Published on: February 26, 2018

26.0K

图表B3 - 一种可解释的图表学习方法,用于预测血脑屏障的透性.

Sumit Kumar1, Shashank Yadav2, Dhvani Sandip Vora3

  • 1Department of Computational Biology, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), Okhla Industrial Estate, Phase III, New Delhi 110020, India.

Briefings in bioinformatics
|December 17, 2025
PubMed
概括
此摘要是机器生成的。

预测药物进入大脑对于治疗神经疾病至关重要. 一个新的基于图形的深度学习模型,graphB3,使用分子原子细节准确地预测血脑屏障 (BBB) 的透性,有助于药物发现.

关键词:
BBB的透性是 BBB 的透性.中枢神经系统疾病 中枢神经系统疾病血脑屏障 血脑屏障 血脑屏障 血脑屏障可以解释的人工智能AI图表 卷积网络 卷积网络

更多相关视频

Predicting In Vivo Payloads Delivery using a Blood-brain Tumor-barrier in a Dish
13:34

Predicting In Vivo Payloads Delivery using a Blood-brain Tumor-barrier in a Dish

Published on: April 16, 2019

9.7K
Analyzing the Permeability of the Blood-Brain Barrier by Microbial Traversal through Microvascular Endothelial Cells
06:26

Analyzing the Permeability of the Blood-Brain Barrier by Microbial Traversal through Microvascular Endothelial Cells

Published on: February 14, 2020

17.1K

相关实验视频

Last Updated: Jan 7, 2026

An In Vivo Blood-brain Barrier Permeability Assay in Mice Using Fluorescently Labeled Tracers
09:35

An In Vivo Blood-brain Barrier Permeability Assay in Mice Using Fluorescently Labeled Tracers

Published on: February 26, 2018

26.0K
Predicting In Vivo Payloads Delivery using a Blood-brain Tumor-barrier in a Dish
13:34

Predicting In Vivo Payloads Delivery using a Blood-brain Tumor-barrier in a Dish

Published on: April 16, 2019

9.7K
Analyzing the Permeability of the Blood-Brain Barrier by Microbial Traversal through Microvascular Endothelial Cells
06:26

Analyzing the Permeability of the Blood-Brain Barrier by Microbial Traversal through Microvascular Endothelial Cells

Published on: February 14, 2020

17.1K

科学领域:

  • 计算化学是一种计算化学.
  • 药物发现 药物发现
  • 神经科学是一个神经科学.

背景情况:

  • 血脑屏障 (BBB) 是一个关键的生物界面,调节物质进入中枢神经系统的通道.
  • 预测BBB透性对于开发治疗大脑疾病的有效疗法至关重要.
  • 当前的深度学习模型通常依赖于有限的分子物理化学特性,阻碍了预测的准确性.

研究的目的:

  • 开发一种新的,参数高效的深度学习模型,用于预测药物血脑屏障 (BBB) 透性.
  • 通过利用药物分子的详细原子信息来改进现有方法.
  • 提供一种可解释的模型,识别影响BBB透的关键分子特征.

主要方法:

  • 实现基于图形卷积的模型,命名为图形B3.3.
  • 使用候选药物的详细原子信息作为输入特征.
  • 在数据集上进行培训和验证,以评估与既有方法对预测性能的评估.

主要成果:

  • 与现有的方法相比,graphB3模型在预测BBB透性方面表现优异.
  • 该模型提供了对BBB交叉至关重要的分子区域的见解.
  • 在识别能够穿越BBB的潜在候选药物方面取得了高准确性.

结论:

  • GraphB3提供了一种强大且易于解释的工具,用于增强与大脑相关疾病的药物发现.
  • 该模型可以加速识别新型血脑屏障 (BBB) 透化合物.
  • 可访问的Web服务器和独立工具促进了graphB3在研发中的应用.