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相关概念视频

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
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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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Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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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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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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Drug-Receptor Bonds01:25

Drug-Receptor Bonds

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Drug-receptor bonds are formed through various chemical forces when drugs interact with target cells. Covalent bonds, strong and irreversible, are exemplified by DNA-alkylating anticancer agents that inhibit cell division. However, such irreversible drug binding lacks selectivity and can modify the DNA of the surrounding healthy cells. Covalent binding often contributes to tissue toxicity, as seen with chloroform and paracetamol metabolites binding to the liver, causing hepatotoxicity.
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A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
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GSL-DTI:图形结构学习网络用于药物向相互作用预测.

Zixuan E1, Guanyu Qiao1, Guohua Wang1

  • 1College of Computer and Control Engineering, Northeast Forestry University,Harbin 150006, China.

Methods (San Diego, Calif.)
|February 15, 2024
PubMed
概括

本研究介绍了GSL-DTI,这是一种用于药物向相互作用预测的自动图形结构学习模型. 它通过自动学习药物-蛋白质对网络结构来提高准确性,优于现有的方法.

科学领域:

  • 计算生物学是一种计算生物学.
  • 生物信息学是一种生物信息学.
  • 机器学习 机器学习

背景情况:

  • 药物向相互作用 (DTI) 的预测对于药物发现至关重要,加速了潜在候选药物的识别.
  • 传统的药物发现是耗时的,昂贵的和高风险的,这使得计算预测方法变得至关重要.
  • 当前基于图形的DTI预测方法通常依赖于网络构建的手动规则,无法捕捉复杂的关系.

研究的目的:

  • 提出GSL-DTI,一种用于增强药物向相互作用预测的自动图形结构学习模型.
  • 在构建药物-蛋白质对网络时克服手动定义规则的局限性.
  • 通过使用先进的图形学习技术,提高DTI预测的准确性和效率.

主要方法:

  • 集成大规模的异质网络,使用基于元路径的图形卷积网络来学习药物和蛋白质表示.
  • 引入自动图形结构学习方法,使用对亲和度得分和分类损失的过门来指导网络构建.
  • 将DTI预测转化为学习药物-蛋白质对网络上的节点分类问题.

主要成果:

  • 在三个公共数据集中,GLS-DTI在DTI预测任务中表现出卓越的表现.
  • 自动图形结构学习方法有效地捕捉了药物和标蛋白之间的潜在关系.
关键词:
药物-蛋白质对 (DPP) 是一种药物-蛋白质对.药物-标相互作用 药物-标相互作用图形结构 学习 学习不同质的信息网络 不同质的信息网络

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  • 该模型为推进DTI预测中的图形结构学习提供了一个新的视角.
  • 结论:

    • 与现有的方法相比,GLS-DTI提供了一种更有效,更准确的方法来预测药物向相互作用.
    • 自动图形结构学习是改进计算药物发现工具的一个有希望的方向.
    • 拟议的模型有可能显著加速药物发现和开发管道.