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相关概念视频

Drug Discovery: Overview01:26

Drug Discovery: Overview

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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Drug-Receptor Interaction: Agonist01:25

Drug-Receptor Interaction: Agonist

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Agonists are drugs that interact with specific receptors in the body to produce a biological response. When an agonist binds to a receptor, it activates or enhances the receptor's function, leading to physiological effects. The interaction between agonist drugs and receptors is crucial for their therapeutic action in various medical treatments.
Agonists can bind to receptors in different ways. Some agonists bind directly to the receptor's active site, mimicking the endogenous...
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Targets for Drug Action: Overview01:26

Targets for Drug Action: Overview

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Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
Receptors are either membrane-spanning or intracellular proteins, which upon binding a ligand, get activated and transmit the signal downstream to elicit a response. Drugs bind receptors, either mimicking the action of endogenous ligands or blocking the receptor activity to bring about a modified response. Nearly 35% of approved drugs target the G...
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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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Drug-Receptor Interactions01:29

Drug-Receptor Interactions

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Drug-receptor interaction describes the binding of receptors by drugs, but not all drug-receptor interactions result in activation and tissue response. For instance, the binding of agonists activates the receptor to generate a cellular reaction, while antagonists bind to receptors without causing their activation.
Several parameters, such as the drug's affinity for its receptor and its efficacy, which is its ability to activate the receptor, determine the drug's effect on the tissue....
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Drug-Receptor Interaction: Antagonist01:28

Drug-Receptor Interaction: Antagonist

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An antagonist is a drug that binds strongly to a receptor without activating it. An antagonist prevents other molecules, such as neurotransmitters or hormones, from binding to the receptor and triggering a cellular response. Such interaction effectively hinders the normal physiological processes mediated by the receptor, resulting in various pharmacological effects depending on the specific receptor targeted.
Antagonists can be classified as competitive or noncompetitive based on their...
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相关实验视频

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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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图表基于神经预训的药物向亲和力预测.

Qing Ye1, Yaxin Sun2,3

  • 1School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, China.

Frontiers in genetics
|October 1, 2024
PubMed
概括

这项研究引入了GNPDTA,这是一种新的图形神经预训练方法,用于药物向 afinity 预测. GNPDTA增强了从未标记的药物和目标数据中提取特征,显著提高了预测准确性.

关键词:
深度神经网络是一个神经网络.药物标亲和力 药物标亲和力特性提取 特性提取图形异态网络的图形同型.预先培训的模型模型

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科学领域:

  • 计算化学是一种计算化学.
  • 生物信息学是一种生物信息学.
  • 机器学习在药物发现中的作用

背景情况:

  • 预训练模型在大量未标记的数据集中表现出色,但在稀缺的药物向相互作用数据中扎.
  • 目前的方法将药物和目标模型单独训练,从而产生不足的特征来准确预测亲和力.

研究的目的:

  • 开发一个改进的图形神经预训基础的药物向亲和度预测方法 (GNPDTA).
  • 通过整合来自未标记和标记药物向数据的信息来增强特征表示.

主要方法:

  • 利用两种预训练模型从药物原子图和目标残留图中提取低级特征.
  • 采用2D卷积神经网络来生成高级药物和目标表示.
  • 将这些表示集成到药物向亲和度估计的预测器中.

主要成果:

  • 与现有的深度学习方法相比,拟议的GNPDTA方法表现出优异的性能.
  • 该方法有效地利用未标记和标记的数据进行增强的特征提取.
  • 实验结果验证了GNPDTA的有效性和更高的准确性.

结论:

  • 通过优化预训练策略,GNPDTA为药物向 afinity 预测提供了一个强大的解决方案.
  • 该方法有效地解决了药物发现中有限的标记数据的挑战.
  • 这种方法具有显著的潜力,可以加速识别新型候选药物.