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

Neuron Structure01:31

Neuron Structure

Overview
Neuron Structure01:30

Neuron Structure

Neurons are the main type of cell in the nervous system that generate and transmit electrochemical signals. They primarily communicate with each other using neurotransmitters at specific junctions called synapses. Neurons come in many shapes that often relate to their function, but most share three main structures: an axon and dendrites that extend out from a cell body.
Structure and Function of Neurons
The neuronal cell body—the soma— houses the nucleus and organelles vital to cellular...
Classification of Neurotransmitters01:30

Classification of Neurotransmitters

Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Network Function of a Circuit01:25

Network Function of a Circuit

Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
Neuronal Communication01:28

Neuronal Communication

Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...

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

Quantum-Centric Alchemical Free Energy Calculations.

Journal of chemical theory and computation·2026
Same author

Molecular Quantum Computations on a Protein.

Journal of chemical theory and computation·2026
Same author

Advancing Reproducibility and Open Data in Theoretical and Computational Chemistry.

Journal of chemical theory and computation·2026
Same author

Automated Force Field Developer and Optimizer Platform: Torsion Reparameterization.

Journal of chemical information and modeling·2026
Same author

QUICK and Robust ESP and RESP Charges for Computational Biochemistry: Open-Source GPU Implementation.

Journal of chemical information and modeling·2026
Same author

Beyond Summary: Reviews That Shape the Field of Computational Molecular Sciences.

Journal of chemical information and modeling·2026
Same journal

Correction to "AstraMEV (AI-Guided Structural Assembly of Multi-Epitope Vaccines) Against Infectious Bronchitis Virus".

Journal of chemical information and modeling·2026
Same journal

MolPy: A Large Language Model-Friendly Toolkit for Reactive Topology Editing in Polymer Simulations.

Journal of chemical information and modeling·2026
Same journal

Molecular Mechanisms of KIT Receptor Dimerization and Oncogenic Activation Revealed by Multiscale Simulations.

Journal of chemical information and modeling·2026
Same journal

Structural and Thermodynamic Discrimination between Agonists and Antagonists of Retinoic Acid Receptor γ and the Vitamin D Receptor.

Journal of chemical information and modeling·2026
Same journal

PACEff Builder: An Efficient Platform for Constructing PACE Hybrid-Resolution Models for Molecular Dynamics Simulations of Aqueous Protein, Peptide Assembly, and Membrane Protein Systems.

Journal of chemical information and modeling·2026
Same journal

TransKla: A Local-Global Cross-Attention Based Transformer Approach for Prediction of Lysine Lactylation Sites.

Journal of chemical information and modeling·2026
查看所有相关文章

相关实验视频

Updated: Jun 7, 2026

Modeling Amyloid-β42 Toxicity and Neurodegeneration in Adult Zebrafish Brain
10:01

Modeling Amyloid-β42 Toxicity and Neurodegeneration in Adult Zebrafish Brain

Published on: October 25, 2017

11.2K

使用神经网络建模复合体

Hongni Jin1, Kenneth M Merz1,2

  • 1Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States.

Journal of chemical information and modeling
|April 8, 2024
PubMed
概括
此摘要是机器生成的。

深度学习模型现在可以预测有机金属复合物的能量. 这种新方法准确地模拟复杂的相互作用,优于探索化学空间的传统方法.

更多相关视频

Preparation of SNS CobaltII Pincer Model Complexes of Liver Alcohol Dehydrogenase
06:31

Preparation of SNS CobaltII Pincer Model Complexes of Liver Alcohol Dehydrogenase

Published on: March 19, 2020

7.0K
Thermochemical Studies of NiII and ZnII Ternary Complexes Using Ion Mobility-Mass Spectrometry
16:11

Thermochemical Studies of NiII and ZnII Ternary Complexes Using Ion Mobility-Mass Spectrometry

Published on: June 8, 2022

2.3K

相关实验视频

Last Updated: Jun 7, 2026

Modeling Amyloid-β42 Toxicity and Neurodegeneration in Adult Zebrafish Brain
10:01

Modeling Amyloid-β42 Toxicity and Neurodegeneration in Adult Zebrafish Brain

Published on: October 25, 2017

11.2K
Preparation of SNS CobaltII Pincer Model Complexes of Liver Alcohol Dehydrogenase
06:31

Preparation of SNS CobaltII Pincer Model Complexes of Liver Alcohol Dehydrogenase

Published on: March 19, 2020

7.0K
Thermochemical Studies of NiII and ZnII Ternary Complexes Using Ion Mobility-Mass Spectrometry
16:11

Thermochemical Studies of NiII and ZnII Ternary Complexes Using Ion Mobility-Mass Spectrometry

Published on: June 8, 2022

2.3K

科学领域:

  • 计算化学是一种计算化学.
  • 材料科学是一种材料科学.
  • 药物发现 药物发现

背景情况:

  • 对分子能量的准确建模对于化学和生物研究至关重要.
  • 深度学习加速了对潜在能量表面的量子化学模型的开发.
  • 现有的深度学习模型主要关注有机分子,因为数据可用性和更简单的电子结构.

研究的目的:

  • 开发一种深度学习架构,用于模拟有机金属复合物的能量.
  • 解决复杂分子系统传统采样方法的局限性.
  • 为了提高预测合体相对能量的准确性.

主要方法:

  • 编制了各种各样的复合物的数据集,使用元动力学进行增强的采样.
  • 开发了一种新的深度学习架构,采用部分电荷来建模远程交互.
  • 利用神经网络潜力进行能量计算.

主要成果:

  • 深度学习模型准确地预测了有机金属复合物的能量.
  • 部分电荷被确定为对复合物的神经网络潜力中长距离相互作用的建模至关重要.
  • 与双混合PWPB95方法相比,开发的模型实现了1.32 kcal/mol的平均绝对误差 (MAE).

结论:

  • 深度学习为模拟复杂有机金属系统的能量提供了一个强大的方法.
  • 包含部分电荷显著提高了对复合体神经网络潜力的准确性.
  • 这项工作推动了对有机金属化合物的化学空间的探索,优于半经验方法.