Feature-Guided Machine Learning for Studying Passive Blood–Brain Barrier Permeability to Aid Drug Discovery

Effective prediction of blood–brain barrier (BBB) permeability remains essential for central nervous system drug development. This study evaluates multiple supervised machine learning models using a public dataset of permeable and non-permeable compounds. Random Forest models demonstrate optimal balance between accuracy and generalizability, outperforming more complex gradient boosting methods that were prone to overfitting. Feature analysis identifies NH/OH and NO group counts as key determinants of passive diffusion, with reduced hydrogen bond donor and heteroatom counts enhancing permeability. Additionally, model performance deteriorates at NH/OH count = 3, establishing this as a decision boundary where hydrogen bonding complexity disrupts reliable prediction. This study shows the non-linear structure-permeability relationships that challenge traditional descriptor-based approaches, while demonstrating that machine learning can simultaneously provide both accurate prediction and applicable insights for drug discovery applications.