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相关概念视频

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

388
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
388
Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Affinity and Avidity

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Overview
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Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
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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: Jul 24, 2025

Bioinformatics Resources for the Study of Glycan-Mediated Protein Interactions
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SS-GNN:一个简单结构的图形神经网络用于亲和力预测.

Shuke Zhang1,2, Yanzhao Jin1,2, Tianmeng Liu1,2

  • 1Software College, Hebei Normal University, Shijiazhuang 050024, China.

ACS omega
|July 3, 2023
PubMed
概括

我们开发了SS-GNN,一个简单的图形神经网络 (GNN) 模型,用于准确的药物标结合亲和力 (DTBA) 预测. 这种高效的模型显著降低了计算成本,并在药物查中实现了最先进的性能.

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Last Updated: Jul 24, 2025

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科学领域:

  • 计算化学是一种计算化学.
  • 药物发现 药物发现
  • 在生物信息学中的机器学习.

背景情况:

  • 准确的药物标结合亲和力 (DTBA) 预测对于有效的药物查至关重要,但在计算上要求很高.
  • 图形神经网络 (GNN) 为复杂的分子相互作用提供了强大的表示能力.

研究的目的:

  • 开发一个基于GNN的计算效率高,准确的模型来预测DTBA.
  • 为了简化蛋白质-连接体相互作用的图形表示,以降低计算成本.

主要方法:

  • 建议SS-GNN,一个简单结构的GNN模型,使用单个无定向图形表示,具有距离值.
  • 在蛋白质表示中忽略了共价键,以进一步降低计算成本.
  • 采用独立的GNN-MLP模块用于原子和边缘特征提取,以及基于边缘的原子对特征聚合和图形聚合.

主要成果:

  • 在仅有60万个参数的模型中实现了最先进的预测性能.
  • 在PDBbind v2016核心集上获得了Pearson的R = 0.853,超过现有的GNN方法5.2%.
  • 证明了高的预测效率,对于典型复合体的亲和力预测只需要0.2 ms.

结论:

  • SS-GNN为DTBA预测提供了一种简化但非常有效的方法.
  • 该模型的效率和准确性使其成为加速药物查过程的宝贵工具.
  • 该代码的开源可用性有助于进一步的研究和应用.