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

34.0K
VSEPR Theory for Determination of Electron Pair Geometries
34.0K
Predicting Products: SN1 vs. SN202:27

Predicting Products: SN1 vs. SN2

13.2K
Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
With increased substitution on the alkyl halide,...
13.2K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

8.2K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
8.2K
Chemical Synapses01:26

Chemical Synapses

8.6K
Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
Because chemical synapses depend on the release of neurotransmitter molecules from synaptic vesicles to pass on their signal, there is an approximately one millisecond delay between when the axon potential reaches the presynaptic terminal and when the neurotransmitter leads to opening of postsynaptic ion channels. Additionally, this signaling is...
8.6K
Multiple Bar Graph01:07

Multiple Bar Graph

5.1K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
5.1K
Chemical Bonds02:40

Chemical Bonds

16.1K

Atoms participate in a chemical bond formation to acquire a completed valence-shell electron configuration similar to that of the noble gas nearest to it in atomic number. Ionic, covalent, and metallic bonds are some of the important types of chemical bonds. Bond energy and bond length determine the strength of a chemical bond.
Types of Chemical Bonds
An ionic bond is formed due to electrostatic attraction between cations and anions. Often, the ions are formed by the transfer of electrons...
16.1K

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

DA-BioNER: data augmentation based on few-shot learning and distant supervision for biomedical named entity recognition.

Bioinformatics (Oxford, England)·2026
Same author

DeepCOI: a large language model-driven framework for fast and accurate taxonomic assignment in animal metabarcoding.

Genome biology·2025
Same author

Intestinal Dysbiosis Caused by Epithelial <i>Fabp6</i> Gene Disruption Exacerbates Gut Inflammatory Disease.

Immune network·2025
Same author

Pseudoballs as a distinct endotype of sinonasal bioballs.

Acta oto-laryngologica·2025
Same author

ApoE deficiency protects from mRNA vaccine-induced mitochondrial dysfunction at the injection site under metabolic stress.

Theranostics·2025
Same author

DeepMobilome: predicting mobile genetic elements using sequencing reads of microbiomes.

Briefings in bioinformatics·2025
Same journal

Interpretable machine learning for Parkinson's disease diagnosis, staging, and biological mechanism exploration: a multicenter analysis.

BioData mining·2026
Same journal

Learning a distance for the clustering of patients with amyotrophic lateral sclerosis.

BioData mining·2026
Same journal

Multi-domain feature fusion with variational mode decomposition and hybrid LightGBM-Logistic Regression for multi-class seizure classification.

BioData mining·2026
Same journal

Large-scale transcriptomic data mining using explainable XGBoost and SHAP reveals shared biomarkers and molecular mechanisms between type-2 diabetes and triple-negative breast cancer for drug repurposing.

BioData mining·2026
Same journal

AVSeg-XAI: Deep learning framework for A/V segmentation with vascular features reveals retinal oculomics as biomarker for cardiovascular disease.

BioData mining·2026
Same journal

Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families.

BioData mining·2026
查看所有相关文章

相关实验视频

Updated: Jun 2, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

366

多Chem:使用多视图图表注意力网络预测化学性质.

Heesang Moon1, Mina Rho2,3,4

  • 1Department of Computer Science, Hanyang University, Seoul, Republic of Korea.

BioData mining
|January 15, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的图形集成深度学习模型,用于有效预测分子性质. 该模型通过捕捉本地和全球结构特征来提高准确性,优于现有方法.

更多相关视频

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.6K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

634

相关实验视频

Last Updated: Jun 2, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

366
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.6K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

634

科学领域:

  • 计算化学计算化学
  • 化学信息学 化学信息学
  • 机器学习 机器学习

背景情况:

  • 对分子性质的准确预测对于药物发现至关重要.
  • 目前的方法往往耗时且昂贵.
  • 深度学习为分子结构提供了先进的洞察力.

研究的目的:

  • 开发一种新,高效,具有成本效益的计算模型来预测分子性质.
  • 利用深度学习来更深入地了解分子结构.
  • 整合本地和全球结构信息,以改善预测.

主要方法:

  • 开发了一个图形集成的多视图学习模型.
  • 用于局部结构特征提取的图表注意层.
  • 利用多头注意层进行全局特征提取.

主要成果:

  • 该模型在9个MoleculeNet数据集上实现了平均AUROC为0.822和RMSE为1.133.
  • 与最先进的方法相比,AUROC的改善率为3%,RMSE的改善率为7%.
  • 在多个数据集和随机种子中评估模型稳定性.

结论:

  • 整合本地和全球结构信息对于准确的分子性质预测至关重要.
  • 开发的模型MultiChem显示了与现有方法相比的显著改进.
  • 模型的稳定性通过广泛的测试得到证实.