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The authors evaluated four supervised machine learning algorithms using 15 RDKit-derived physicochemical descriptors across 2,039 compounds from the MoleculeNet BBBP benchmark to predict central nervous system drug permeability. A grid-search-optimized XGBoost classifier achieved superior predictive performance, delivering an 88.97% accuracy, 97.76% recall, and an ROC-AUC of 0.9282 (0.8982 ± 0.0130 across 5-fold cross-validation). By integrating SHAP explainability analyses, the framework confirms that topological polar surface area (TPSA), hydrogen bond donor count (HBD), and lipophilicity (LogP) govern blood-brain barrier transport decisions, providing a transparent alternative to black-box molecular representations.
Predicting blood-brain barrier permeability does not require complex graph neural networks: fifteen standard physicochemical descriptors paired with an optimized XGBoost model achieve a 0.928 ROC-AUC and 97.8% recall with full SHAP-derived mechanistic transparency.
Blood-brain barrier (BBB) permeability is a critical determinant in the development of central nervous system therapeutics because it directly influences the ability of drug candidates to reach their target sites within the brain. In this study, an explainable machine learning framework was developed to predict BBB permeability using molecular descriptors generated from the MoleculeNet BBBP dataset with the RDKit cheminformatics toolkit. Fifteen physicochemical descriptors extracted from 2,039 compounds were used to train four supervised machine learning algorithms, including Logistic Regression, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). Hyperparameter optimization was performed using GridSearchCV, while model interpretability was investigated using SHapley Additive exPlanations (SHAP). Among the evaluated models, the optimized XGBoost classifier achieved the best predictive performance, with an accuracy of 88.97%, a precision of 88.92%, a recall of 97.76%, an F1-score of 93.13%, and a ROC-AUC of 0.9282. Stratified five-fold cross-validation further demonstrated the robustness of the proposed model, yielding a mean ROC-AUC of 0.8982 +/- 0.0130. Feature importance and SHAP analyses consistently identified TPSA, HBD, and LogP as the most influential molecular descriptors governing BBB permeability prediction. Overall, the proposed framework provides an accurate, interpretable, and computationally efficient approach for BBB permeability prediction and may serve as a valuable tool for the early-stage screening of CNS drug candidates.