Predicting the Brain-To-Plasma Unbound Partition Coefficient of Compounds via Formula-Guided Network.

Journal: Journal of chemical information and modeling
Published Date: May 26, 2025

Abstract

Blood-brain barrier (BBB) permeability plays a crucial role in determining drug efficacy in the brain, with the brain-to-plasma unbound partition coefficient () recognized as a key parameter of BBB permeability in drug development. However, data are scarce and mostly in-house. In predicting the generality and applicability of existing empirical scoring models remain underexplored. To address this, we established a public rat data set through data mining and developed a formula-guided deep learning model, CMD-FGKpuu, which performed well on multiple benchmark tests, marking good demonstration of the potential of deep learning for prediction. Additionally, the model can be fine-tuning with project-specific experimental data, thus improving its practical utility. The findings offer an effective tool for predicting BBB permeability in drug development and introduce a new perspective for applying few-shot learning in the pharmaceutical field.

Authors

  • Yurong Zou
    Institute of Functional Molecules, College of Chemistry and Life Science, Chengdu Normal University, Chengdu 611130, China. zyrlia1018@163.com.
  • Haolun Yuan
    MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu 610064, China.
  • Zhongning Guo
    State Key Laboratory of Biotherapy and Collaborative Innovation Center of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Tao Guo
    Key Laboratory of Digital Technology in Medical Diagnostics of Zhejiang Province, Hangzhou, Zhejiang, China.
  • Zhiyuan Fu
    School of Resources Environment and Tourism, Anyang Normal University, Anyang 455000, China.
  • Ruihan Wang
    College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, China.
  • Dingguo Xu
    MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, PR China; Research Center for Material Genome Engineering, Sichuan University, Chengdu, Sichuan 610065, PR China. Electronic address: dgxu@scu.edu.cn.
  • Qiantao Wang
    Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China. qwang@scu.edu.cn.
  • Taijin Wang
    Chengdu Zenitar Biomedical Technology Co., Ltd., Chengdu 610045, China.
  • Lijuan Chen
    State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan 610041, PR China. Electronic address: chenlijuan125@163.com.

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