Abstract
The clinical translation of nanoparticle-based treatments remains limited due
to the unpredictability of (nanoparticle) NP
pharmacokinetics$\unicode{x2014}$how they distribute, accumulate, and clear
from the body. Predicting these behaviours is challenging due to complex
biological interactions and the difficulty of obtaining high-quality
experimental datasets. Existing AI-driven approaches rely heavily on
data-driven learning but fail to integrate crucial knowledge about NP
properties and biodistribution mechanisms. We introduce a multi-view deep
learning framework that enhances pharmacokinetic predictions by incorporating
prior knowledge of key NP properties such as size and charge into a
cross-attention mechanism, enabling context-aware feature selection and
improving generalization despite small datasets. To further enhance prediction
robustness, we employ an ensemble learning approach, combining deep learning
with XGBoost (XGB) and Random Forest (RF), which significantly outperforms
existing AI models. Our interpretability analysis reveals key physicochemical
properties driving NP biodistribution, providing biologically meaningful
insights into possible mechanisms governing NP behaviour in vivo rather than a
black-box model. Furthermore, by bridging machine learning with physiologically
based pharmacokinetic (PBPK) modelling, this work lays the foundation for
data-efficient AI-driven drug discovery and precision nanomedicine.