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相关概念视频

The Two-State Receptor Model01:29

The Two-State Receptor Model

2.0K
The two-state receptor model explains a drug's interaction with receptors, such as G protein-coupled receptors and ligand-gated ion channels, to induce or inhibit a biological response. When no natural ligands are present, a receptor exists in an equilibrium of inactive (Ri) and active (Ra) conformations. The inactive form does not produce a response, while the active form generates a basal effect known as constitutive activity.
The binding affinity of a drug determines its interaction with...
2.0K
Drug-Receptor Interactions01:29

Drug-Receptor Interactions

5.3K
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....
5.3K
Targets for Drug Action: Overview01:26

Targets for Drug Action: Overview

6.3K
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...
6.3K
Drug-Receptor Bonds01:25

Drug-Receptor Bonds

2.9K
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.
In...
2.9K
Ligand Binding Sites02:40

Ligand Binding Sites

12.9K
Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
12.9K
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

734
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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相关实验视频

Updated: Jul 11, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

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多模式对比表示学习用于药物标结合亲缘关系预测.

Linlin Zhang1, Chunping Ouyang1, Yongbin Liu1

  • 1School of Computer, University of South China, Hengyang, China.

Methods (San Diego, Calif.)
|November 12, 2023
PubMed
概括
此摘要是机器生成的。

准确的药物标结合亲和力 (DTA) 预测对于药物开发至关重要. 一个新的多式联络深度学习模型,FMDTA,有效地整合了各种药物和目标数据,优于现有的增强DTA预测方法.

关键词:
相反的学习学习.深度学习 (Deep Learning) 是一种深度学习.药物目标结合 亲和力 亲和力多模式融合多模式融合

更多相关视频

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

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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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相关实验视频

Last Updated: Jul 11, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

217
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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科学领域:

  • 生物医学信息学 生物医学信息学
  • 计算机化药物发现技术
  • 机器学习在药理学中的应用

背景情况:

  • 药物的疗效依赖于标相互作用,使得药物标结合亲和力 (DTA) 预测对于药物开发至关重要.
  • 传统的DTA预测方法与大数据需求扎;深度学习显示出希望,但通常使用单个数据模式.
  • 整合来自药物和目标的多式联络信息可以产生更全面,更准确的DTA预测.

研究的目的:

  • 引入FMDTA,一种新的多式联络信息融合模型,用于药物标结合亲缘关系预测.
  • 利用药物和目标信息的字符串和图形模式,以改善DTA预测.
  • 通过使用对比学习和利用对齐信息来平衡多式联运数据来增强特征表示.

主要方法:

  • 开发了FMDTA,这是一个深度学习模型,集成了药物和目标的字符串和图表表示.
  • 采用对比学习来平衡跨不同模式的特征表示.
  • 利用药物原子和目标残留物之间的对齐信息来捕获SMILES和目标序列中的位置模式.

主要成果:

  • 与最先进的模型相比,FMDTA在两个基准数据集上表现出更好的表现.
  • 该模型通过整合多式联运数据,有效地捕获了有价值的特征信息.
  • 实验结果验证了FMDTA的可行性和强大的特征提取能力.

结论:

  • 通过融合多式联络数据,FMDTA提供了一种强大的方法,可以通过融合多式联络数据,准确地预测药物标的结合亲和力.
  • 该模型能够整合多种数据类型并捕获位置信息的能力提高了其预测能力.
  • FMDTA代表了计算药物发现的重大进步,代码和数据公开可用.