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Updated: Sep 15, 2025

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在 silico 中,使用可解释的深度学习方法预测 pK a 值

Chen Yang1, Changda Gong1, Zhixing Zhang1

  • 1Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.

Journal of pharmaceutical analysis
|July 18, 2025
PubMed
概括

使用图形神经网络和分子指纹,GraFpKa准确地预测分子酸解离常数 (pKa) 值. 这种可解释的模型通过可视化影响pKa的关键分子特征来帮助药物发现.

关键词:
注意力FPFP的注意力FP的注意力深度学习是一种深度学习.图形神经网络是一个神经网络.在 silico 预测预测.集成梯度的集成梯度是指集成的梯度.在pKa pKa pKa

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