Jove
Visualize
联系我们

相关概念视频

Molecular Models02:00

Molecular Models

40.5K
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.
40.5K

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

PyMolGen: Database-Driven Molecular Generation of Drug-Like Compounds.

Journal of chemical information and modeling·2026
Same author

Automated Molecular Design in BRADSHAW, Applied to the Optimization of ERAP1 Inhibitors.

Journal of medicinal chemistry·2026
Same author

Query Matters: How Selection Strategies Influence Active Learning in Drug Discovery.

Journal of chemical information and modeling·2026
Same author

A Multiomic Liquid Biopsy for the Earlier Detection of Colorectal Cancer.

Cancer prevention research (Philadelphia, Pa.)·2025
Same author

Physics-Based Solubility Prediction for Organic Molecules.

Chemical reviews·2025
Same author

Exploring BERT for Reaction Yield Prediction: Evaluating the Impact of Tokenization, Molecular Representation, and Pretraining Data Augmentation.

Journal of chemical information and modeling·2025
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
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

相关实验视频

Updated: Sep 13, 2025

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
08:21

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

2.7K

基准测试基于3D结构的分子发生器

Natasha Sanjrani1,2, Damien E Coupry1, Peter Pogány1

  • 1Department of Cheminformatics, Research Technologies, GSK, Gunnels Wood Road, Stevenage SG1 2NY, U.K.

Journal of chemical information and modeling
|July 25, 2025
PubMed
概括
此摘要是机器生成的。

深度学习生成器在结构有效性方面扎,而组合方法则很慢. 一个新的基准强调了基于结构的药物设计生成器的改进领域.

更多相关视频

Modeling an Enzyme Active Site using Molecular Visualization Freeware
14:37

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Published on: December 25, 2021

10.2K
Interactive Molecular Model Assembly with 3D Printing
06:15

Interactive Molecular Model Assembly with 3D Printing

Published on: August 13, 2020

10.2K

相关实验视频

Last Updated: Sep 13, 2025

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
08:21

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

2.7K
Modeling an Enzyme Active Site using Molecular Visualization Freeware
14:37

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Published on: December 25, 2021

10.2K
Interactive Molecular Model Assembly with 3D Printing
06:15

Interactive Molecular Model Assembly with 3D Printing

Published on: August 13, 2020

10.2K

科学领域:

  • *计算化学和化学信息学.
  • * 药物发现中的人工智能.
  • * 分子建模和模拟.

背景情况:

  • *评估药物设计的生成模型至关重要.
  • *现有的生成3D分子的方法有局限性.
  • * 蛋白质-连接体相互作用和形状是药物设计的关键目标.

研究的目的:

  • *为了对3D组合和深度学习生成器进行基准测试.
  • * 评估它们重现蛋白质-连接体相互作用和形状的能力.
  • * 确定不同生成方法的优点和缺点.

主要方法:

  • *使用BindingMOAD数据集开发了一个新的基准.
  • * 评估了顺序图神经网络 (Pocket2Mol,PocketFlow),扩散模型 (DiffSBDD,MolSnapper) 和遗传算法 (AutoGrow4,LigBuilderV3). 通过测试,我们发现了这些神经网络的特征.
  • * 评估了结构有效性,3D连接体构造和相互作用复制.

主要成果:

  • * 深度学习模型未能生成结构有效的分子和构造.
  • * 组合方法是缓慢的,并且产生了无法通过二维波器检测的分子.
  • * 确定了深度学习和组合方法的具体局限性.

结论:

  • * 深度学习生成器需要更好地关注结构有效性和交互准确性.
  • * 组合发电机需要优化速度和过器合规性.
  • * 该基准为推进基于结构的药物设计生成器提供了一个框架.