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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

389
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
389

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

Organocatalytic Asymmetric Michael Addition/Annulation Cascade toward the β-Nitro Chromones.

The Journal of organic chemistry·2026
Same author

Stereoselective Dihalogenation of Alkynes to Access Enantioenriched <i>Z</i>-Vicinal Dihaloalkene Atropisomers.

Journal of the American Chemical Society·2025
Same author

Enantioselective gold(I)-catalysed alkyne hydroarylations for inherently chiral calix[4]arenes.

Chemical communications (Cambridge, England)·2025
Same author

A multi-dimensional computational framework of drug-induced hepatotoxicity: integrating molecular structure features with disease pathogenesis.

Briefings in bioinformatics·2025
Same author

Asymmetric synthesis of quinolinone-based polycyclic indoles through [1,3]-rearrangement/cyclization reaction.

Chemical communications (Cambridge, England)·2025
Same author

Enantioselective Catalytic Synthesis of Inherently Chiral Calixarenes.

Chemical record (New York, N.Y.)·2025

相关实验视频

Updated: Jan 16, 2026

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K

一个多输入深度学习架构用于STAT3抑制剂预测.

Kairui Liang1,2, Wenling Qin3, Yonghong Zhang1

  • 1Chongqing Key Research Laboratory for Drug Metabolism, College of Pharmacy, Chongqing Medical University, Chongqing 400016, China.

ACS omega
|October 6, 2025
PubMed
概括

我们开发了一种新的机器学习模型,可以准确地预测信号转换器和转录3 (STAT3) 抑制剂的激活器. 这种先进的模型提高了药物发现的预测准确性和可解释性.

更多相关视频

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

1.4K

相关实验视频

Last Updated: Jan 16, 2026

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K
Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

1.4K

科学领域:

  • 计算化学计算化学
  • 机器学习 机器学习
  • 药物发现 药物发现 药物发现

背景情况:

  • 信号转换器和转录3激活器 (STAT3) 在生理和瘤途径中至关重要.
  • 现有的用于STAT3抑制剂预测的机器学习模型需要提高性能和可解释性.

研究的目的:

  • 开发一种先进的机器学习模型,用于预测STAT3抑制剂.
  • 提高STAT3抑制剂查的预测性能和可解释性.

主要方法:

  • 引入了一个指纹增强图 (FPG) 注意网络模型.
  • 集成基于序列的指纹和图表注意力网络,用于特征学习.
  • 利用多层感知子进行分子活动分类.

主要成果:

  • 在49个测试模型中,FPG模型实现了最佳预测性能 (AUC=0.897).
  • 在识别STAT3抑制剂方面表现优于现有的预测模型.
  • 通过揭示结构-活动关系,SHAP算法和注意热图增强了模型的解释性.

结论:

  • FPG模型为STAT3抑制剂发现提供了卓越的预测准确性和可解释性.
  • 开发的网络服务 (STAT3 Pro) 促进了STAT3抑制剂预测的进一步研究和应用.