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相关概念视频

Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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Sequence Networks of Rotating Machines

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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相关实验视频

Updated: Jan 7, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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限制分子设计的双向强化学习神经网络.

Junan Lin1, Jiří Hostaš2, Anguang Hu3

  • 1Digital Technologies Research Centre, National Research Council Canada, Toronto, ON, Canada. junan.lin@nrc-cnrc.gc.ca.

Scientific reports
|December 24, 2025
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概括

我们开发了BiRLNN,这是一个双向框架,使用循环神经网络和强化学习来进行分子设计. 这种方法优化了类似药物的特性,并更有效地探索化学空间以发现药物.

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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:

  • 在药物发现中,优化类似药物的特性至关重要.
  • 现有的分子设计框架在探索化学空间方面存在局限性.
  • 确保生成的分子的语法有效性至关重要.

研究的目的:

  • 介绍BiRLNN,一个分子设计的双向框架.
  • 增强化学空间的探索,用于药物发现.
  • 为了优化产生的化合物的药物样性质.

主要方法:

  • 利用自我引用嵌入式字符串进行分子表示.
  • 实现了一种双向循环神经网络架构.
  • 应用强化学习,具有用于微调的多目标奖励功能.

主要成果:

  • BiRLNN确保生成的分子100%的语法有效性.
  • 这种双向的方法使得有限的化学空间能够得到平衡的探索.
  • 强化学习成功地将一代引导到可取的复合类.

结论:

  • 在多目标药物设计中,BiRLNN为导航化学空间提供了一个强大的战略.
  • 该框架改善了药物类似性质的优化.
  • BiRLNN促进了新药候选药物的发现.