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

Predicting Molecular Geometry02:27

Predicting Molecular Geometry

46.1K
VSEPR Theory for Determination of Electron Pair Geometries
46.1K
Extraction: Advanced Methods00:56

Extraction: Advanced Methods

1.2K
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
1.2K
Kinetic Molecular Theory and Gas Laws Explain Properties of Gas Molecules02:34

Kinetic Molecular Theory and Gas Laws Explain Properties of Gas Molecules

37.6K
The test of the kinetic molecular theory (KMT) and its postulates is its ability to explain and describe the behavior of a gas. The various gas laws (Boyle’s, Charles’s, Gay-Lussac’s, Avogadro’s, and Dalton’s laws) can be derived from the assumptions of the KMT, which have led chemists to believe that the assumptions of the theory accurately represent the properties of gas molecules.
37.6K
Molecular and Ionic Solids02:54

Molecular and Ionic Solids

20.3K
Crystalline solids are divided into four types: molecular, ionic, metallic, and covalent network based on the type of constituent units and their interparticle interactions.
Molecular Solids
Molecular crystalline solids, such as ice, sucrose (table sugar), and iodine, are solids that are composed of neutral molecules as their constituent units. These molecules are held together by weak intermolecular forces such as London dispersion forces, dipole-dipole interactions, or hydrogen bonds, which...
20.3K
Molecular Orbital Theory II03:51

Molecular Orbital Theory II

27.7K
Molecular Orbital Energy Diagrams
27.7K
Prediction Intervals01:03

Prediction Intervals

3.4K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.4K

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

CMA-Nano: A DNA Methylation Detection Method for Nanopore Sequencing Data Based on a Cross-Modal Attention Mechanism.

ACS omega·2026
Same author

An Explainable Deep Learning Framework Integrating DNA Sequence and Transcription Initiation Signals for Gene Expression Prediction.

ACS synthetic biology·2026
Same author

Microencapsulated OPO Enhances Intestinal SCFA Production by Optimizing Lipid Digestion and Regulating Bile Acid Metabolism in Mice.

Foods (Basel, Switzerland)·2026
Same author

LysePred: A Multiscale Convolutional Neural Network for Predicting Hemolytic Activity of Antimicrobial Peptides.

ACS synthetic biology·2026
Same author

An Interpretable Deep Learning Framework Leveraging RNA Foundation Model and Capsule Networks for Accurate Prediction of RNA 2'-O-Methylation Sites.

Journal of chemical information and modeling·2026
Same author

A Foundation Model for Capturing Complexity of Menstrual Health Data.

npj women's health·2026
Same journal

Measuring drug similarity using drug-drug interactions.

Quantitative biology (Beijing, China)·2026
Same journal

A feature extraction framework for discovering pan-cancer driver genes based on multi-omics data.

Quantitative biology (Beijing, China)·2026
Same journal

DDI-Transform: A neural network for predicting drug-drug interaction events.

Quantitative biology (Beijing, China)·2026
Same journal

Functional predictability of universal gene circuits in diverse microbial hosts.

Quantitative biology (Beijing, China)·2026
Same journal

SimHOEPI: A resampling simulator for generating single nucleotide polymorphism data with a high-order epistasis model.

Quantitative biology (Beijing, China)·2026
Same journal

Plasma proteome profiling reveals biomarkers of chemotherapy resistance in patients with advanced colorectal cancer.

Quantitative biology (Beijing, China)·2026
查看所有相关文章

相关实验视频

Updated: Feb 13, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.9K

对于分子性质预测的先进深度学习方法.

Chao Pang1,2, Henry H Y Tong3, Leyi Wei1,3

  • 1School of Software Shandong University Jinan China.

Quantitative biology (Beijing, China)
|February 12, 2026
PubMed
概括
此摘要是机器生成的。

深度学习通过预测分子性质来加速药物发现. 像图形神经网络和变压器这样的先进方法,以及转移学习等策略,是实现这一进步的关键.

关键词:
数据集数据集数据集深度学习是一种深度学习.分子性质预测分子性质预测分子表示的分子表征.

更多相关视频

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.6K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.5K

相关实验视频

Last Updated: Feb 13, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.9K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.6K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.5K

科学领域:

  • 计算化学是一种计算化学.
  • 化学信息学 化学信息学
  • 人工智能在药物发现中的作用

背景情况:

  • 准确的分子性质预测对于有效的药物发现至关重要.
  • 计算方法,特别是深度学习,为加速这一过程提供了一个有希望的途径.
  • 深度学习模型利用大型数据集而无需广泛的功能工程.

研究的目的:

  • 审查分子表示和数据集用于属性预测.
  • 为分子性质预测提供先进的深度学习方法.
  • 突出这一领域的挑战和未来方向.

主要方法:

  • 对分子表示和数据集的审查.
  • 深度学习模型的概述,包括图形神经网络和基于变压器的网络.
  • 讨论深度学习策略:3D预训练,对比学习,多任务学习,转移学习和元学习.

主要成果:

  • 深度学习模型显示了分子性质预测的巨大潜力.
  • 适用于各种先进的深度学习架构和策略.
  • 确定的挑战包括数据稀缺,信息利用效率低下和疾病特异性.

结论:

  • 深度学习提供了一种强大的方法来提高药物发现中的分子性质预测.
  • 需要进一步的研究来解决目前的局限性,并提高模型的通用性.
  • 优化数据集和模型策略对于未来的进步至关重要.