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

Aldehydes and Ketones with HCN: Cyanohydrin Formation Mechanism01:10

Aldehydes and Ketones with HCN: Cyanohydrin Formation Mechanism

4.2K
Cyanohydrins are formed when cyanide nucleophiles and carbonyl compounds like aldehydes and ketones react. A strong base, the cyanide ion, catalyzes cyanohydrin formation. The ions are generated from HCN under aqueous conditions. Once the cyanide ions are generated, the first step involves the nucleophilic attack of the cyanide ions on the electrophilic carbonyl carbon. This attack shifts the π electrons from the C=O to the oxygen atom forming the alkoxide ion intermediate. The alkoxide anion...
4.2K
Aldehydes and Ketones with HCN: Cyanohydrin Formation Overview01:32

Aldehydes and Ketones with HCN: Cyanohydrin Formation Overview

3.8K
Cyanohydrins are compounds that contain –CN and –OH groups on the same carbon atom. They are formed by the nucleophilic addition of the cyanide ions to the carbonyl group. Cyanide ions are highly basic and nucleophilic and can be generated from HCN under aqueous conditions. However, since HCN is a weak acid, the number of cyanide ions generated is very small. Hence, a small amount of base or KCN/NaCN is added to HCN to increase the concentration of the cyanide ions in the reaction...
3.8K
Protecting Groups for Aldehydes and Ketones: Introduction01:23

Protecting Groups for Aldehydes and Ketones: Introduction

8.8K
Protecting groups are compounds that can bind to a specific functional group in the presence of other functional groups to protect them from undesired chemical reactions. These compounds can selectively bind to particular functional groups and advance chemoselective reactions in polyfunctional systems (Figure 1). After the functional group has served its purpose, it is removed by reacting it with specific compounds.
8.8K
Diels–Alder vs Retro-Diels–Alder Reaction: Thermodynamic Factors01:31

Diels–Alder vs Retro-Diels–Alder Reaction: Thermodynamic Factors

5.8K
The Diels–Alder reaction is thermally reversible, meaning that the reaction reverts to the starting diene and dienophile under suitable temperatures. The forward reaction gives a cyclohexene derivative and is favored at low to medium temperatures. The reverse process, also called retro-Diels–Alder reaction, is a ring-opening process favored at high temperatures.
5.8K
Synthetic Biology02:55

Synthetic Biology

5.5K
Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...
5.5K
Ketones with Nonenolizable Aromatic Aldehydes: Claisen–Schmidt Condensation01:01

Ketones with Nonenolizable Aromatic Aldehydes: Claisen–Schmidt Condensation

4.4K
Benzaldehyde, like formaldehyde, lacks an α hydrogen and cannot enolize to form an enolate. Hence, the reaction of benzaldehyde with a ketone in the presence of an aqueous base forms a single crossed product. This reaction is referred to as Claisen–Schmidt condensation.
As the self-condensation of ketones is generally not favored in basic conditions, the self-condensed products do not form in the reaction between ketones and benzaldehyde. The general reaction of Claisen–Schmidt...
4.4K

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

Generative design of programmable asymmetric β-barrel nanopores.

bioRxiv : the preprint server for biology·2026
Same author

SE(3)-equivariant ternary complex prediction towards target protein degradation.

Nature communications·2025
Same journal

Demonstration of a quantum C-NOT gate in a time-multiplexed fully reconfigurable photonic processor.

Nature communications·2026
Same journal

Nonlinear quantum light source with van der Waals ferroelectric NbOX<sub>2</sub> (X = Br, I).

Nature communications·2026
Same journal

Antagonistic histone H2A variants and autonomous heterochromatin formation shape epigenomic patterns in Arabidopsis.

Nature communications·2026
Same journal

The long tail of nitrate pollution in groundwater challenges governance of global water quality.

Nature communications·2026
Same journal

Select microbial metabolites promote tau aggregation in a murine tauopathy model.

Nature communications·2026
Same journal

Warming climate has lengthened global intense tropical cyclone seasons.

Nature communications·2026
查看所有相关文章

相关实验视频

Updated: Jan 16, 2026

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods
05:34

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Published on: June 6, 2025

1.6K

基于化学知识的框架,用于隐私意识的回复合成学习.

Guikun Chen1, Xu Zhang1, Xiaolin Hu2

  • 1The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, Zhejiang, China.

Nature communications
|September 25, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了基于机器学习的回复合成的隐私保护框架. 基于化学知识的框架 (CKIF) 允许在不共享敏感化学反应数据的情况下进行分布式培训.

更多相关视频

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

Author Spotlight: Accelerating Discovery in Microporous Material Chemistry

Published on: October 6, 2023

4.3K

相关实验视频

Last Updated: Jan 16, 2026

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods
05:34

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Published on: June 6, 2025

1.6K
Author Spotlight: Accelerating Discovery in Microporous Material Chemistry
07:20

Author Spotlight: Accelerating Discovery in Microporous Material Chemistry

Published on: October 6, 2023

4.3K

科学领域:

  • 计算化学是一种计算化学.
  • 机器学习是机器学习.
  • 化学信息学 化学信息学

背景情况:

  • 化学反应数据对于制药,材料科学和工业化学的进步至关重要.
  • 专有反应数据需要严格保密,这对当前的机器学习培训范式构成了挑战.
  • 现有的训练回复合成模型的方法涉及从多个来源汇总数据,由于数据传输和潜在暴露,造成重大隐私风险.

研究的目的:

  • 开发一种保护隐私的方法来训练基于机器学习的回归合成模型.
  • 在不影响专有数据机密性的情况下,在多个组织中实现回复合成模型的分布式训练.
  • 引入基于化学知识的框架 (CKIF) 以确保安全有效的模型培训.

主要方法:

  • 开发了基于化学知识的框架 (CKIF),用于维护隐私的分布式培训.
  • CKIF以代方式汇总模型参数,避免需要共享原始反应数据.
  • 利用预测反应物的化学特性来评估模型行为并确定适应性聚合重量.

主要成果:

  • CKIF成功地实现了跨组织的回复合成模型的分布式培训.
  • 该框架保持了专有化学反应数据的机密性.
  • 在各种反应数据集上,CKIF表现出优越的性能,与几种强有力的基线方法相比.

结论:

  • 基于化学知识的框架 (CKIF) 为保护隐私的回复合成模型培训提供了安全有效的解决方案.
  • 在处理敏感的化学反应数据时,CKIF解决了对保密的关键需求.
  • 这种方法有助于在没有数据泄露的情况下在化学研究中开发协作模型.