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相关概念视频

Purposive Learning01:22

Purposive Learning

86
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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In-vitro Mutagenesis01:16

In-vitro Mutagenesis

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To learn more about the function of a gene, researchers can observe what happens when the gene is inactivated or “knocked out,” by creating genetically engineered knockout animals. Knockout mice have been particularly useful as models for human diseases such as cancer, Parkinson’s disease, and diabetes.
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Conservative Site-specific Recombination and Phase Variation02:53

Conservative Site-specific Recombination and Phase Variation

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Because the DNA segments are cut and reorganized in a direction-specific manner, site-specific recombination has emerged as an efficient genetic engineering technique. Flippase and Cyclization recombinases or Flp and Cre, respectively, are two members of the tyrosine recombinase family derived from bacteriophages, that are used to mediate site-specific DNA insertions, deletions, and targeted expression of proteins in mammalian cell lines.
The recognition sites for Cre recombinase called LoxP...
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相关实验视频

Updated: May 9, 2025

A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning
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A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning

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有针对性的分子生成与潜在的强化学习学习.

Ragy Haddad1, Eleni E Litsa2, Zhen Liu2,3

  • 1Cellarity, Inc, Somerville, USA. rhaddad@cellarity.com.

Scientific reports
|April 30, 2025
PubMed
概括

这项研究引入了一种新的深度学习方法,使用强化学习来优化潜空间中的分子,帮助药物发现. 该方法有效地产生具有所需性质和亚结构的分子,表现出具有竞争力的性能.

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
  • 机器学习用于分子设计.

背景情况:

  • 药物发现依赖于产生具有特定性质的分子.
  • 深度学习生成模型提供先进的分子设计能力.
  • 在计算上优化分子通常需要明确的化学规则.

研究的目的:

  • 引入一种新的计算方法,利用强化学习优化分子.
  • 在分子生成中绕过明确化学规则的需要.
  • 通过设计具有所需性质和子结构的分子来增强药物发现.

主要方法:

  • 在预训练生成模型的潜在空间中利用近接政策优化,一种强化学习算法.
  • 该方法通过潜伏空间导航,以确定与具有所需性质的分子相应的区域.
  • 将优化框架与自动编码器模型架构配对,展示了建筑不可知论.

主要成果:

  • 在分子优化基准上实现了与最先进的方法相匹配或优越的性能.
  • 成功生成了具有预先指定的亚结构的分子,同时优化了分子性质.
  • 在不同的生成模型架构中证明了该方法的有效性和多功能性.

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相关实验视频

Last Updated: May 9, 2025

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A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning

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Using Optogenetics to Reverse Neuroplasticity and Inhibit Cocaine Seeking in Rats
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Using Optogenetics to Reverse Neuroplasticity and Inhibit Cocaine Seeking in Rats

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Stereotaxic Injection of a Viral Vector for Conditional Gene Manipulation in the Mouse Spinal Cord
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结论:

  • 拟议的强化学习框架为药物发现中的分子优化提供了一种高效和灵活的方法.
  • 这种方法有助于设计具有目标性质和特定结构特征的新型分子.
  • 这种方法对加速计算药物设计和开发具有重大前景.