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相关概念视频

Reduction of Alkenes: Asymmetric Catalytic Hydrogenation02:17

Reduction of Alkenes: Asymmetric Catalytic Hydrogenation

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Catalytic hydrogenation of alkenes is a transition-metal catalyzed reduction of the double bond using molecular hydrogen to give alkanes. The mode of hydrogen addition follows syn stereochemistry.
The metal catalyst used can be either heterogeneous or homogeneous. When hydrogenation of an alkene generates a chiral center, a pair of enantiomeric products is expected to form. However, an enantiomeric excess of one of the products can be facilitated using an enantioselective reaction or an...
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Catalytically Perfect Enzymes01:07

Catalytically Perfect Enzymes

4.0K
The theory of catalytically perfect enzymes was first proposed by W.J. Albery and J. R. Knowles in 1976. These enzymes catalyze biochemical reactions at high-speed. Their catalytic efficiency values range from 108-109 M-1s-1. These enzymes are also called 'diffusion-controlled' as the only rate-limiting step in the catalysis is that of the substrate diffusion into the active site. Examples include triose phosphate isomerase, fumarase, and superoxide dismutase.
 
Most enzymes...
4.0K
Induced-fit Model01:13

Induced-fit Model

80.9K
Most chemical reactions in cells require enzymes—biological catalysts that speed up the reaction without being consumed or permanently changed. They reduce the activation energy needed to convert the reactants into products. Enzymes are proteins, that usually work by binding to a substrate—a reactant molecule that they act upon.
Enzymes exhibit substrate specificity, meaning that they can only bind to certain substrates. This is mainly determined by the shape and chemical...
80.9K
Sharpless Epoxidation02:57

Sharpless Epoxidation

4.0K
The conversion of allylic alcohols into epoxides using the chiral catalyst was discovered by K. Barry Sharpless and is known as Sharpless epoxidation. The use of a chiral catalyst enables the formation of one enantiomer of the product in excess. This chiral catalyst is mainly a chiral complex of titanium tetraisopropoxide and tartrate ester (specific stereoisomer). The stereoisomer used in the chiral catalyst dictates the formation of the enantiomer of the product. In other words, the use of...
4.0K
Regioselectivity of Electrophilic Additions-Peroxide Effect02:35

Regioselectivity of Electrophilic Additions-Peroxide Effect

8.6K
In the presence of organic peroxides, the addition of hydrogen bromide to an alkene yields the isomer that is not predicted by Markovnikov’s rule. For example, the addition of hydrogen bromide to 2-methylpropene in the presence of peroxides gives 1-bromo-2-methylpropane. This addition reaction proceeds via a free radical mechanism, which reverses the regioselectivity. The free radical reaction mechanism involves three stages: initiation, propagation, and termination.
8.6K
Enzymes02:34

Enzymes

81.6K
Inside living organisms, enzymes act as catalysts for many biochemical reactions involved in cellular metabolism. The role of enzymes is to reduce the activation energies of biochemical reactions by forming complexes with its substrates. The lowering of activation energies favor an increase in the rates of biochemical reactions.
Enzyme deficiencies can often translate into life-threatening diseases. For example, a genetic abnormality resulting in the deficiency of the enzyme G6PD...
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相关实验视频

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Development of Heterogeneous Enantioselective Catalysts using Chiral Metal-Organic Frameworks MOFs
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在选择性建模中对非对称催化剂的2D描述器进行介绍.

Pavel Sidorov1, Nobuya Tsuji1

  • 1Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo, 001-0021, Japan.

Chemistry (Weinheim an der Bergstrasse, Germany)
|November 27, 2023
PubMed
概括

机器学习加速了化学中的新型化合物设计. 本研究探讨了在不对称催化剂中有效建模催化剂选择性的2D描述器,克服了计算成本.

科学领域:

  • 化学 化学 化学
  • 计算化学计算化学
  • 机器学习 机器学习

背景情况:

  • 传统的不对称催化模型依赖于物理理解,需要昂贵的量子化学计算.
  • 现有的方法限制了用于催化剂设计的大型数据集的in-silico选.

研究的目的:

  • 以突出利用二维结构建模催化剂选择性的进展.
  • 展示二维描述器作为一种低成本,高速的替代品,用于in-silico选.

主要方法:

  • 使用2D描述符,如拓索引,分子指纹和碎片.
  • 使用定量结构与属性关系 (QSPR) 工作流程.
  • 模型构建和验证技术.

主要成果:

  • 2D描述器在计算速度和成本方面提供了显著的优势.
  • 这些方法使广泛的化学数据能够有效地进行in-silico选.
  • 在不对称的催化设计中证明了应用.

结论:

  • 基于2D的模型是催化剂发现中快速选的最佳选择.
  • 进一步的研究可以提高对这些简化模型的机制理解.
关键词:
不对称的催化剂.机器学习是机器学习.量化结构与选择性关系的关系.

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  • 使用2D描述符的机器学习是设计新型催化剂的强大工具.