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Updated: Jun 15, 2025

Visualizing Protein Kinase A Activity In Head-fixed Behaving Mice Using In Vivo Two-photon Fluorescence Lifetime Imaging Microscopy
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Published on: June 7, 2019

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GR-pKa:一个传递信息的神经网络,具有用于pKa预测的保留机制

Runyu Miao1, Danlin Liu2,3, Liyun Mao1

  • 1Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, No. 130, Meilong Road, Xuhui District, Shanghai, 200237, China.

Briefings in bioinformatics
|August 22, 2024
PubMed
概括

一个新的GR-pKa模型使用图形神经网络和量子力学属性准确预测分子酸解离常数 (pKa). 这种方法有助于药物发现,改善了对药物特性 (如吸收,分布,新陈代谢,分泌和毒性) 的预测 (ADMET).

关键词:
深度学习是一种深度学习.多真实性学习的多真实性学习pKa 预测的预测保持机制的保留机制.

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