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相关概念视频

Transformers in Distribution System01:27

Transformers in Distribution System

159
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
159

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Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
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使用变压器进行基于序列的虚拟选.

Shengyu Zhang1, Donghui Huo1,2, Robert I Horne1

  • 1Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK.

Nature communications
|July 28, 2025
PubMed
概括
此摘要是机器生成的。

灵转换器 (Ligand-Transformer) 是一种深度学习方法,可以准确地预测蛋白质-灵结合亲和力和构造变化. 这种方法通过识别强效抑制剂和理解分子相互作用,有助于药物发现.

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A Protocol for Computer-Based Protein Structure and Function Prediction
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科学领域:

  • 计算化学是一种计算化学.
  • 分子生物学分子生物学
  • 药物发现 药物发现

背景情况:

  • 蛋白质-配体相互作用对于生物过程和药物设计至关重要.
  • 深度学习为实验方法提供了一个具有成本效益的替代方案,用于识别候选药物.

研究的目的:

  • 介绍Ligand-Transformer,这是一种新的深度学习方法,用于预测蛋白质-配体结合亲和力.
  • 使用基于序列的方法来预测分子相互作用和结构变化.

主要方法:

  • 开发了Ligand-Transformer,这是一个使用变压器架构的深度学习模型.
  • 输入蛋白质氨基酸序列和小分子拓,以预测结合亲和力.
  • 应用该模型来选EGFR抑制剂并分析ABL激酶抑制剂的影响.

主要成果:

  • 鉴定出具有较低纳米分子功率的强效抑制剂,对突变EGFRLTC激酶.
  • 成功预测了由ABL激酶抑制剂诱导的结构变化.
  • 证明了联刚转换器能够预测结合亲和力和自由能量格局变化的能力.

结论:

  • 连接物转换器准确地预测蛋白质和小分子相互作用,包括结合亲和力.
  • 该方法促进了分子机制的发现,并加速了早期药物设计.
  • 基于序列的预测使得在连接体结合时的形状变化可以进行表征.