QSAR Prediction of BBB Permeability Based on Machine Learning upon PETBD: A Novel Data Set of PET Tracers

Assessing small-molecule blood-brain barrier permeability is laborious, yet critical in drug development. Quantitative prediction models are hindered by a lack of high-quality data set. To address this, we curated PETBD, a novel data set of drug concentrations for 1056 positron emission tomography tracers across 14 organs at 60 min post injection, as well as in vivo metadata. We developed machine learning models to predict the brain-to-blood concentration ratio (log BB), and for the first time, drug concentration in the brain. Extreme gradient boosting model reached the best performance in predicting C_brain (R² = 0.700) and also achieved state-of-the-art log BB prediction (R² = 0.770). Feature importance analysis was employed to explain the contributions of physicochemical-based features. The model's superior generalizability was validated against the B3DB benchmark and with unpublished PET tracers. The open-access PETBD data set (https://github.com/GDUT-Computer-Medical-Science-Team/PETBD-QSAR/) and models promise to accelerate CNS drug discovery by bridging computational predictions with empirical pharmacokinetic outcomes.