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

Experimental Determination of Chemical Formula02:37

Experimental Determination of Chemical Formula

37.5K
The elemental makeup of a compound defines its chemical identity, and chemical formulas are the most concise way of representing this elemental makeup. When a compound’s formula is unknown, measuring the mass of its constituent elements is often the first step in determining the formula experimentally.
37.5K
Synthesis and Decomposition Reactions02:17

Synthesis and Decomposition Reactions

32.1K
Synthesis and decomposition are two types of redox reactions. Synthesis means to make something, whereas decomposition means to break something. The reactions are accompanied by chemical and energy changes. 
32.1K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

8.1K
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,...
8.1K
Synthesis of α-Substituted Carbonyl Compounds: The Stork Enamine Reaction01:26

Synthesis of α-Substituted Carbonyl Compounds: The Stork Enamine Reaction

3.3K
α-Substituted ketones or aldehydes can be synthesized from enamines by the Stork enamine reaction, named after its pioneer Gilbert Stork. Enamines are useful synthetic intermediates where the lone pair on nitrogen is in conjugation with the C=C bond. They resemble enolate ions, as the resonance forms of both species have a nucleophilic α carbon.
3.3K
Cycloaddition Reactions: MO Requirements for Thermal Activation01:16

Cycloaddition Reactions: MO Requirements for Thermal Activation

3.5K
Thermal cycloadditions are reactions where the source of activation energy needed to initiate the reaction is provided in the form of heat. A typical example of a thermally-allowed cycloaddition is the Diels–Alder reaction, which is a [4 + 2] cycloaddition. In contrast, a [2 + 2] cycloaddition is thermally forbidden.
3.5K
Cycloaddition Reactions: MO Requirements for Photochemical Activation01:12

Cycloaddition Reactions: MO Requirements for Photochemical Activation

2.0K
Some cycloaddition reactions are activated by heat, while others are initiated by light. For example, a [2 + 2] cycloaddition between two ethylene molecules occurs only in the presence of light. It is photochemically allowed but thermally forbidden.
2.0K

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

ProphDR: An Interpretable Deep Learning Model for Predicting Cancer Drug Response via Multi-Omics and Cross-Attention Mechanisms.

Journal of chemical information and modeling·2026
Same author

Prochlorperazine dimaleate elicits antiproliferative activity of gastric cancer cells via inhibiting the PI3K/AKT/mTOR signaling pathway.

Genes & diseases·2026
Same author

TPS-Flow: Physics-Guided Flow-Based Generative Modeling of Protein Transition Paths.

Journal of chemical information and modeling·2026
Same author

Spatiotemporal profiling of white matter lesions and their contribution in the pathologies of Parkinson's disease animal models.

GeroScience·2026
Same author

Generative AI for controllable protein sequence design: A survey.

npj drug discovery·2026
Same author

Unified heterogeneity-aware benchmark of drug synergy prediction: a cross-study analysis of traditional machine learning and graph deep learning models.

Journal of cheminformatics·2026

相关实验视频

Updated: May 6, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

16.2K

Syn-MolOpt:一种基于合成计划的分子优化方法,使用数据衍生功能反应模板.

Xiaodan Yin1,2, Xiaorui Wang1, Zhenxing Wu1

  • 1College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.

Journal of cheminformatics
|March 2, 2025
PubMed
概括

这项研究介绍了Syn-MolOpt,这是一种新的分子优化方法,它优先考虑了可合成性以及所需的特性. 通过使用功能反应模板,Syn-MolOpt有效地生成优化且易于合成的候选药物.

关键词:
代谢性质优化优化 代谢性质优化分子优化分子优化反应模板的反应模板.综合的规划 综合的规划有毒性优化优化 有毒性优化

更多相关视频

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

2.5K
Author Spotlight: Accelerating Discovery in Microporous Material Chemistry
07:20

Author Spotlight: Accelerating Discovery in Microporous Material Chemistry

Published on: October 6, 2023

4.2K

相关实验视频

Last Updated: May 6, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

16.2K
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

2.5K
Author Spotlight: Accelerating Discovery in Microporous Material Chemistry
07:20

Author Spotlight: Accelerating Discovery in Microporous Material Chemistry

Published on: October 6, 2023

4.2K

科学领域:

  • 计算化学和化学信息学
  • 药物发现和开发 药物发现和开发
  • 化学中的人工智能.

背景情况:

  • 在药物开发中,分子优化对于增强候选药物的特性至关重要.
  • 现有的深度学习方法经常忽视分子合成能力,导致不切实际的化合物.
  • 需要优化算法来平衡物业改进与合成可行性.

研究的目的:

  • 开发一种以合成规划为导向的分子优化方法,以解决可合成性差距.
  • 介绍Syn-MolOpt,一种新的方法,利用功能反应模板进行属性特定的优化.
  • 评估Syn-MolOpt在多属性优化任务中的性能,包括毒性和新陈代谢.

主要方法:

  • 开发了一个通用管道,用于构建属性特定的功能反应模板库.
  • 引入了Syn-MolOpt,它利用这些模板来引导分子优化到所需的特性和合成能力.
  • 在四个多属性优化任务 (GSK3β-突变性,GSK3β-hERG,GSK3β-CYP3A4,GSK3β-CYP2C19) 上对基准模型进行了 Syn-MolOpt 的评估.

主要成果:

  • 在各种分子优化任务中,Syn-MolOpt的表现优于三个基准模型 (Modof,HierG2G,SynNet).
  • 可视化证实了功能反应模板在指导优化和生成合成路径方面的有效性.
  • 即使在得分准确度有限的情况下,Syn-MolOpt也表现出强大的性能,这表明它可以在现实世界中应用.

结论:

  • Syn-MolOpt成功地将分子优化与合成规划相结合,产生优化但可合成的化合物.
  • 该方法能够设计属性特定的功能反应模板库,并提供参考合成路径,这是一个关键的创新.
  • Syn-MolOpt为药物发现中的各种分子优化挑战提供了一个有价值和可适应的工具.