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

Molecular Models02:00

Molecular Models

38.4K
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
38.4K
Molecular Orbital Theory I02:35

Molecular Orbital Theory I

32.1K
Overview of Molecular Orbital Theory
32.1K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

34.4K
VSEPR Theory for Determination of Electron Pair Geometries
34.4K
Hybridization of Atomic Orbitals I03:24

Hybridization of Atomic Orbitals I

47.1K
The mathematical expression known as the wave function, ψ, contains information about each orbital and the wavelike properties of electrons in an isolated atom. When atoms are bound together in a molecule, the wave functions combine to produce new mathematical descriptions that have different shapes. This process of combining the wave functions for atomic orbitals is called hybridization and is mathematically accomplished by the linear combination of atomic orbitals. The new orbitals that...
47.1K
MO Theory and Covalent Bonding02:40

MO Theory and Covalent Bonding

10.5K
The molecular orbital theory describes the distribution of electrons in molecules in a manner similar to the distribution of electrons in atomic orbitals. The region of space in which a valence electron in a molecule is likely to be found is called a molecular orbital. Mathematically, the linear combination of atomic orbitals (LCAO) generates molecular orbitals. Combinations of in-phase atomic orbital wave functions result in regions with a high probability of electron density, while...
10.5K
Hybridization of Atomic Orbitals II03:35

Hybridization of Atomic Orbitals II

32.3K
sp3d and sp3d 2 Hybridization
32.3K

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

A deep learning-based classification method for subclinical zonular laxity in AS-OCT images.

Frontiers in cell and developmental biology·2026
Same author

Achieving 1.0-s Thermally Activated Delayed Fluorescence via Synergistic Control of Reverse Intersystem Crossing and Exciton Cycling.

Angewandte Chemie (International ed. in English)·2026
Same author

Differences in hepatocyte-related indicators within occupational hazardous factor exposure between genders.

Frontiers in public health·2026
Same author

A High-Affinity Antibody for Rapid Screening of PFAS: Breaking Immunological Inertness through Descriptor-Guided Hapten Design.

Analytical chemistry·2026
Same author

Multiferroic-Centric Materials and Systems Engineering for Battery Applications: An Insight Into Mechanisms, Strategies, and Characterizations.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Investigating magnetostatics of Fe-Ni nanosphere assemblies by electron holography and micromagnetic simulations.

Nanoscale·2026
Same journal

Complementing Onsager's Conductivity Theory by Grotthuss Mechanism Mitigation via Ion-Induced Depletion of Hydrogen-Bond-Donating Water.

Journal of chemical theory and computation·2026
Same journal

Microscopic Stress in Biomembranes: A Perspective on Key Concepts, Methods, and Applications.

Journal of chemical theory and computation·2026
Same journal

Analytic Nuclear Gradients Including Oriented External Electric Fields in a Molecule-Fixed Frame.

Journal of chemical theory and computation·2026
Same journal

Knowledge Distillation of a Protein Language Model Yields a Foundational Implicit Solvent Model.

Journal of chemical theory and computation·2026
Same journal

Generalizable Protein Folding Pathway Exploration with DA2-GRASP: Extending Beyond Miniproteins.

Journal of chemical theory and computation·2026
Same journal

Improving PCM in Protic Media: Markov State Models for TD-DFT Calculations.

Journal of chemical theory and computation·2026
查看所有相关文章

相关实验视频

Updated: Jul 8, 2025

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

1.1K

为量子计算机编码分子对接.

Jinyin Zha1,2, Jiaqi Su1, Tiange Li1

  • 1Beijing QBoson Quantum Technology Co., Ltd., Beijing 100015, China.

Journal of chemical theory and computation
|December 13, 2023
PubMed
概括
此摘要是机器生成的。

量子计算加速了药物发现的分子对接. 新的方法,网点匹配 (GPM) 和特征原子匹配 (FAM),为量子解答器编码对接问题,使小分子和的虚拟选速度更快.

更多相关视频

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

175
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

583

相关实验视频

Last Updated: Jul 8, 2025

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

1.1K
Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

175
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

583

科学领域:

  • 计算化学是一种计算化学.
  • 量子计算应用程序 量子计算应用程序
  • 药物的发现和开发.

背景情况:

  • 分子对接是药物发现中至关重要但计算密集的技术.
  • 经典计算机在处理精确分子对接所需的广泛搜索空间方面面临限制.
  • 加快分子对接对于高效的虚拟药物查至关重要.

研究的目的:

  • 开发用于加速分子对接的新型计算方法.
  • 为了利用量子计算来解决分子对接问题.
  • 为了提高药物发现虚拟查的效率.

主要方法:

  • 介绍了网点匹配 (GPM) 和特征原子匹配 (FAM) 算法.
  • 将分子对接编码为正方形不受约束的二进制优化 (QUBO) 模型.
  • 使用量子计算机,特别是连贯的Ising机器 (CIM),用于解决QUBO模型.

主要成果:

  • 网点匹配 (GPM) 证明了与Glide SP.等既定方法相比的采样能力.
  • 据估计,与传统计算机相比,在一个连贯的Ising机器 (CIM) 上,所提出的方法的速度是1000倍.
  • 在量子计算的 QUBO 模型中成功编码了分子对接.

结论:

  • 开发的GPM和FAM方法为使用量子计算机的分子对接提供了显著的加速.
  • 这些量子加速方法有可能彻底改变对小分子和的虚拟药物查.
  • 这项工作为更高效,更可扩展的药物发现管道铺平了道路.