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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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Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

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The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
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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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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 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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Protein-Drug Binding: Mechanism and Kinetics01:16

Protein-Drug Binding: Mechanism and Kinetics

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Protein-drug binding refers to the interaction between drugs and proteins within the body. This binding process can occur intracellularly, involving drug interactions with enzymes or receptors within cells, or extracellularly, involving plasma proteins in the blood.
Various forces drive these interactions, including hydrogen bonds, hydrophobic interactions, ionic bonds, electrostatic interactions, and van der Waals forces. These bonds enable drugs to bind to specific sites on proteins,...
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相关实验视频

Updated: Jan 14, 2026

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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一种可扩展和强大的集体深度学习方法,用于预测药物向相互作用.

Zhixing Cheng1, Qunfang Yan1, Dewu Ding2

  • 1School of Science, Jiangnan University, Wuxi, 214000, China.

Interdisciplinary sciences, computational life sciences
|October 21, 2025
PubMed
概括

EDeepDTI是一个集体深度学习框架,通过整合多源功能来增强药物向相互作用 (DTI) 的预测. 这种方法提高了计算药物发现的准确性和通用性.

关键词:
深度学习是一种深度学习.药物向相互作用的预测和预测组合学习学习 组合学习多视图功能提供了多视图功能.预先训练的模型模型.

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Diagonal Method to Measure Synergy Among Any Number of Drugs
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Diagonal Method to Measure Synergy Among Any Number of Drugs

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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: Jan 14, 2026

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

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Diagonal Method to Measure Synergy Among Any Number of Drugs
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Diagonal Method to Measure Synergy Among Any Number of Drugs

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科学领域:

  • 计算生物学是一种计算生物学.
  • 药理学 药理学是指药理学的学科.
  • 机器学习是机器学习.

背景情况:

  • 准确的药物向相互作用 (DTI) 识别对于有效的药物发现至关重要.
  • 计算方法加速药物开发,但难以整合各种数据来进行高精度的DTI预测.

研究的目的:

  • 引入EDeepDTI,这是一个集体深度学习框架,用于增强DTI预测.
  • 通过多视图功能集成,提高DTI预测的准确性和通用性.

主要方法:

  • EDeepDTI使用多个分子指纹来获取药物结构信息.
  • 先进的预训练模型产生了丰富的药物和蛋白质特征 (结构,语义).
  • 用深度学习组合学习,为每个特征配对和贪聚合提供基础学习者.

主要成果:

  • 在多个数据集和预测任务中,EDeepDTI的表现始终优于基线方法.
  • 该框架在DTI预测中展示了卓越的性能,稳定性和可扩展性.
  • 与现有方法相比,EDeepDTI的变体也显示出了显著的改进.

结论:

  • EDeepDTI有效地集成了多视图功能,用于高精度的DTI预测.
  • 集体深度学习方法提高了计算药物发现效率.
  • EDeepDTI提供了一种强大且可扩展的解决方案,用于识别药物向相互作用.