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Transducer Mechanism: Enzyme-Linked Receptors

Transducer Mechanism: Enzyme-Linked Receptors

Enzyme-linked receptors are cell-surface receptors acting as an enzyme or associating with an enzyme intracellularly. They make excellent drug targets. Drugs can bind to the extracellular ligand-binding domain or directly affect their enzymatic domain and alter their activity.
Major types that are helpful drug targets include:

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Updated: Jul 14, 2025

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

Published on: October 23, 2019

人工智能方法在酶标分析:进步和挑战.

Shukai Gu¹, Huanxiang Liu², Liwei Liu³

¹College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.

Drug discovery today

|October 7, 2023

概括

机器学习和深度学习模型改善了用于药物发现的酶分析. 这些定量结构-活性关系 (QSAR) 方法有助于优化药物和预测副作用.

关键词:

在QSAR中使用QSAR.深度学习是一种深度学习.图形中性网络网络的图形中性网络基因酶概况分析 (kinase profiling) 是一种方法.机器学习是机器学习.

更多相关视频

Characterization at the Molecular Level using Robust Biochemical Approaches of a New Kinase Protein

Characterization at the Molecular Level using Robust Biochemical Approaches of a New Kinase Protein

Published on: June 30, 2019

Identification of Kinase-substrate Pairs Using High Throughput Screening

Identification of Kinase-substrate Pairs Using High Throughput Screening

Published on: August 29, 2015

相关实验视频

Last Updated: Jul 14, 2025

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

Published on: October 23, 2019

Characterization at the Molecular Level using Robust Biochemical Approaches of a New Kinase Protein

Characterization at the Molecular Level using Robust Biochemical Approaches of a New Kinase Protein

Published on: June 30, 2019

Identification of Kinase-substrate Pairs Using High Throughput Screening

Identification of Kinase-substrate Pairs Using High Throughput Screening

Published on: August 29, 2015

科学领域:

生物化学和分子生物学
计算化学的计算化学
药理学药理学是指药理学的学科.

背景情况:

激酶调节细胞过程,使它们成为关键的治疗点.
精确的激酶分析对于药物发现至关重要,解决选择性和预测副作用.

研究的目的:

审查机器学习 (ML) 和深度学习 (DL) 基于定量结构-活动关系 (QSAR) 模型的进步.
突出ML/DL中的趋势,挑战和未来方向.

主要方法:

关于ML和DL在酶分析中的应用的最新文献的综述.
专注于定量结构-活动关系 (QSAR) 模型.

主要成果:

基于ML和DL的QSAR模型显示,在激酶分析方面取得了显著进展.
这些模型有助于优化,药物重新定位和理解药物的副作用.

结论:

基于ML/DL的QSAR模型是用于激酶药物发现的强大工具.
未来的工作应该集中在实验数据集构建和先进模型架构上.