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

Blood–brain
barrier (BBB) permeability plays a
crucial role
in determining drug efficacy in the brain, with the brain-to-plasma
unbound partition coefficient (Kp,uu)
recognized as a key parameter of BBB permeability in drug development.
However, Kp,uu data are scarce and mostly
in-house. In predicting Kp,uu the generality
and applicability of existing empirical scoring models remain underexplored.
To address this, we established a public rat Kp,uu 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 Kp,uu 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.