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Updated: Feb 14, 2026

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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动态-GLEP:一种基于动态的深度学习框架,用于在代表性A类GPCR中预测配体效率

Zhiyi Chen1,2, Yongxin Hao2,3, Yuhong Su4

  • 1School of Life Sciences, Nanjing University, 163 Xianlin Avenue, Nanjing 210023, China.

Briefings in bioinformatics
|February 12, 2026
PubMed
概括

动态-GLEP是一种新的计算框架,通过分析分子动力学,准确地预测G蛋白合受体 (GPCR) 的药物疗效. 这种方法通过捕捉功能选择性至关重要的结构变化来增强药物发现.

关键词:
在GPCR连接剂的有效性效果.构成组合的构成组合.深度学习是一种深度学习.分子动力学分子动力学基于结构的药物设计.转移学习转移学习

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