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相关概念视频

Conserved Binding Sites01:49

Conserved Binding Sites

4.1K
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...
4.1K
Ligand Binding Sites02:40

Ligand Binding Sites

12.6K
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...
12.6K
Drug Discovery: Overview01:26

Drug Discovery: Overview

7.3K
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...
7.3K
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

480
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...
480
Targets for Drug Action: Overview01:26

Targets for Drug Action: Overview

6.0K
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

2.7K
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...
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相关实验视频

Updated: May 24, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

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

Published on: February 23, 2024

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GFLearn:用于药物目标绑定亲和力预测的通用特征学习.

Zibo Huang, Xinrui Weng, Le Ou-Yang

    IEEE journal of biomedical and health informatics
    |March 3, 2025
    PubMed
    概括

    一个新的通用特征学习 (GFLearn) 模型通过准确预测药物-标结合亲和力来增强药物发现,即使对于新药和标来说也是如此. 这种深度学习方法提高了预测的稳定性和通用性.

    科学领域:

    • 计算化学是一种计算化学.
    • 药理学 药理学是指药理学的学科.
    • 人工智能的人工智能是人工智能.

    背景情况:

    • 准确预测药物标结合亲和力对于有效的药物发现和开发至关重要.
    • 目前的深度学习模型经常因数据依赖而难以预测新药或目标的亲和力.
    • 当模型遇到数据分布转移时,性能退化是常见的.

    研究的目的:

    • 开发一种新的通用特征学习 (GFLearn) 模型,用于强大的药物标结合亲和力预测.
    • 提高未见药物和目标预测模型的通用性.
    • 为了减轻因药物发现中的数据分布转移而导致的性能下降.

    主要方法:

    • 将图形神经网络 (GNN) 与自主监督的不变特征学习模块集成.
    • 从药物和点分子中提取强大和可泛化的特征.
    • 在新药,新标和综合场景中对各种数据集进行广泛的实验验证.

    主要成果:

    • GFLearn模型在预测新的药物和目标的结合性亲和性方面始终超过了最先进的方法.
    • 在各种预测任务中证明了稳定性,并通过交叉数据集评估验证了可通用性.
    • 案例研究证实了针对特定药物标对的准确预测,有助于药物查和重新使用.

    更多相关视频

    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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    Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
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    Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

    Published on: December 1, 2020

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    相关实验视频

    Last Updated: May 24, 2025

    Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
    10:21

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

    Published on: February 23, 2024

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    Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
    06:50

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

    Published on: January 26, 2024

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    Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
    08:31

    Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

    Published on: December 1, 2020

    4.9K

    结论:

    • GFLearn模型在预测药物标结合亲和力方面取得了重大进展,特别是对于新型实体.
    • 它处理数据分发转移的能力提高了现实世界药物发现应用程序的可靠性.
    • GFLearn为加速药物查和重新定位工作提供了宝贵的见解.