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相关概念视频

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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Conserved Binding Sites01:49

Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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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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相关实验视频

Updated: Jul 2, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

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用扩展图表学习-卷积网络来预测药物目标亲和力.

Haiou Qi1, Ting Yu2, Wenwen Yu3

  • 1Nursing Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China.

BMC bioinformatics
|February 16, 2024
PubMed
概括

这项研究引入了GLCN-DTA,这是一种用于药物标亲和力预测的新型深度学习模型. GLCN-DTA增强了分子图表的表示,提高了药物发现的准确性和稳定性.

关键词:
深度学习是一种深度学习.药物发现 药物发现药物目标亲和力预测图形学习 - 卷积网络

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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 机器学习

背景情况:

  • 计算机辅助药物设计 (CADD) 利用高性能计算用于制药研究.
  • 药物向亲和力 (DTA) 预测加速了化合物查,降低了成本和资源使用.
  • 深度学习提高了DTA预测的准确性,基于图形的方法显示了全面数据表示的希望.

研究的目的:

  • 在基于图形的DTA预测中解决固定相邻矩阵的局限性.
  • 开发一种能够从蛋白质和药物分子图表中学习更丰富的结构信息的模型.
  • 提高DTA预测模型的概括能力.

主要方法:

  • 介绍GLCN-DTA,这是一个集成图形学习模块与图形卷积的模型.
  • 学习软相邻矩阵来完善分子图的上下文结构.
  • 利用图形卷积来增强蛋白质和药物结构的特征表示.

主要成果:

  • 在DTA预测任务中,GLCN-DTA表现出卓越的稳定性和准确性.
  • 与传统的固定相邻矩阵方法相比,该模型有效地学习了更丰富的结构信息.
  • 实验验证证证实了GLCN-DTA在各种场景中的有效性.

结论:

  • GLCN-DTA通过协同图形学习和图形卷积来提高DTA预测,以获得更丰富的表示.
  • 该模型的重点是改善特征表示,而不是区分蛋白质分类.
  • 对于结构上有序的蛋白质,GLCN-DTA显示出潜在的有效性,对于内在无序的蛋白质可能存在局限性.