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相关概念视频

Ziegler–Natta Chain-Growth Polymerization: Overview01:17

Ziegler–Natta Chain-Growth Polymerization: Overview

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Ziegler–Natta polymerization is another form of addition or chain‐growth polymerization used for synthesizing linear polymers over branched polymers. The catalyst used for polymerization is the Ziegler–Natta catalyst, named after Karl Ziegler and Giulio Natta, who developed it in 1953. This catalyst is an organometallic complex of titanium tetrachloride and triethyl aluminum, with the active form of the catalyst being an alkyl titanium compound. Using the Ziegler–Natta...
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Cationic Chain-Growth Polymerization: Mechanism00:57

Cationic Chain-Growth Polymerization: Mechanism

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The cationic polymerization mechanism consists of three steps: initiation, propagation, and termination. In the initiation step of the polymerization process, the π bond of a monomer gets protonated by the Lewis acid catalyst, which is formed from boron trifluoride and water. The protonation of the π bond generates a carbocation stabilized by the electron‐donating group. In the propagation step, the π bond of the second monomer acts as a nucleophile and attacks the...
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Radical Chain-Growth Polymerization: Overview01:10

Radical Chain-Growth Polymerization: Overview

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Chain-growth or addition polymerization is successive addition reactions of monomers with a polymer chain. In radical chain-growth polymerization, the reaction proceeds via a free-radical intermediate. The free radical is formed from radical initiators, which spontaneously generate free radicals by homolytic fission. Organic peroxides (such as dibenzoyl peroxide, as shown in Figure 1) or azo compounds are popular radical initiators. A low concentration ratio of radical initiator to monomer is...
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Radical Chain-Growth Polymerization: Chain Branching01:17

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The skeletal structure of polymers synthesized via radical polymerization is always branched. For example, the polymerization of ethylene by radical polymerization results in a low-density grade of polyethylene with a heavily branched skeletal structure. Here, the radical site abstracts hydrogen from the growing chain, and the radical site shifts from the end (a primary carbon center) to anywhere within the growing chain (a secondary carbon center). Consequently, the part of the chain from the...
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Carrier generation is the process by which electron-hole pairs (EHPs) are created within the semiconductor. In direct-bandgap semiconductors, such as gallium arsenide (GaAs), this occurs efficiently when energy absorption prompts valence electrons to leap into the conduction band, leaving behind holes.
This process is given by the generation rate G and is efficient due to the conservation of momentum between the valence band maximum and conduction band minimum.
Indirect generation involves an...
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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
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MolRWKV:使用局部增强和图形增强的条件分子生成模型.

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  • 1School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China.

Journal of computational chemistry
|April 10, 2025
PubMed
概括

我们介绍了MolRWKV,这是一个新的分子生成模型,它使用RWKV语言模型来创建具有特定属性的分子. 这种方法提高了条件生成的准确性,并产生了具有目标蛋白亲和性的多种分子.

关键词:
在美国,CNN是CNN.全国CNN是什么意思MolRWKVV 在线观看在RWKVV中使用.有条件的分子生成.

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科学领域:

  • 计算化学是一种计算化学.
  • 人工智能在药物发现中的作用
  • 分子建模分子建模

背景情况:

  • 条件分子生成对于开发具有特定应用所需性质的分子至关重要.
  • 语言模型,特别是循环波形转换 (RWKV),由于它们的并行处理和推理能力,在序列生成任务中表现有希望.
  • 现有的新分子生成方法在准确控制条件性质和保持分子多样性方面经常面临挑战.

研究的目的:

  • 提出 MolRWKV,一个新的 de novo 条件分子生成模型.
  • 将卷积神经网络 (CNN) 和图形卷积网络 (GCN) 与RWKV模型集成,以增强分子特征提取.
  • 评估MolRWKV在无条件和条件分子生成任务中的性能.

主要方法:

  • 通过在RWKV框架内集成CNN用于本地SMILES序列特征提取和GCN用于分子图形拓结构分析,开发了MolRWKV模型.
  • 采用一种基于循环的代币生成策略,这种策略是语言模型特征,用于新的分子合成.
  • 进行了实验,将MolRWKV与无条件和条件生成场景中的现有模型进行比较.

主要成果:

  • 在无条件和条件分子生成方面,MolRWKV取得了与最先进模型相似的结果.
  • 在条件分子生成任务中表现出更高的准确性.
  • 成功生成了多种分子,同时保留了基本的支架信息.
  • 生成的分子表现出对特定标蛋白的亲和力.

结论:

  • MolRWKV代表了条件分子生成的有希望的进步,利用RWKV,CNN和GCN的优势.
  • 该模型能够提高条件准确性,产生多样化的支架,并产生特定目标分子,这突显了其在药物发现和材料科学方面的潜力.
  • 进一步的研究可以探索扩展模型的架构和训练数据集以实现更广泛的应用.