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基于深度学习的药物半衰期分类,以增强药物开发和药理动力学

Affaf Khaouane1, Hadjer Barki1, Samira Ferhat1

  • 1Laboratory of Biomaterial and transport Phenomena (LBMPT), University of Médéa, Pole Urbain, 26000, Médéa, Algeria.

Advanced pharmaceutical bulletin
|March 16, 2026
PubMed
概括

这项研究引入了一种用于预测药物半衰期的新分类方法,比传统回归模型提高了准确性和临床解释性. 该方法有效地将药物分为短半衰期和长半衰期组,以便更好地进行药理动力学分析.

关键词:
二元分类二元分类二元分类.深度学习是一种深度学习.药物发现 药物发现药物的半衰期 药物的半衰期功能提取 功能提取机器学习是机器学习.神经网络的神经网络的神经网络

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