Predicting Blood–Brain Barrier Permeability from Experimental Data: An Interpretable and Externally Validated Machine Learning Framework
4 affiliations
- 1School of Materials Science and Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
- 2Department of Geriatrics, Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, 9 Skłodowskiej Curie Str., 85-094 Bydgoszcz, Poland
- 3Department of Toxicology and Bromatology, Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, 2 Jurasza Str., 85-089 Bydgoszcz, Poland
- 4Department of Organic and Physical Chemistry, Medical University of Warsaw, 1 Banacha Str., 02-097 Warsaw, Poland
Abstract
Background: The blood–brain barrier (BBB), which restricts the brain penetration of most small molecules and almost all biologics, continues to be a significant hurdle in the development of drugs for the central nervous system (CNS). During early-stage screening, a reliable computational prediction of BBB permeability, typically expressed as log BB, can help reduce the experimental load. Methods: We provide a well-validated machine learning system created solely using the B3DB experimental database, which includes 7807 chemicals with BBB+/BBB− annotations and 1058 compounds with in vivo log BB values. Using the Mordred library, a carefully selected set of 40 two-dimensional chemical descriptors was calculated from SMILES notation without the use of artificial data augmentation. Stratified five-fold cross-validation was used to comprehensively benchmark the nine methods used in this study. Results: On a held-out test set (n = 212), gradient boosting produced the greatest regression performance, with R2 = 0.6043, RMSE = 0.4740 log units, and MAE = 0.3326, which is in line with the upper range recorded for experimental BBB datasets. On an internal test set (n = 1562), the corresponding classifier obtained an AUC-ROC of 0.9476 and a balanced accuracy of 0.8568; on an independent external validation set (n = 175), it achieved an AUC-ROC of 0.9137. Topological polar surface area was found by SHAP analysis to be the primary factor influencing BBB permeability, with lipophilicity and ionization-related characteristics being the second and third most important factors, respectively. Nonlinear relationships in accordance with accepted pharmacokinetic principles were validated using partial dependence analysis. Conclusion: This study provides a reliable technique for predicting BBB permeability in CNS drug discovery.
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BibTeX
@article{tiwari2026predicting,
title = {{Predicting Blood–Brain Barrier Permeability from Experimental Data: An Interpretable and Externally Validated Machine Learning Framework}},
author = {Saurabh Tiwari and Katarzyna Mądra-Gackowska and Marcin Gackowski and Nokeun Park and Łukasz Szeleszczuk},
journal = {Pharmaceutics},
year = {2026},
month = may,
doi = {10.3390/pharmaceutics18060670},
url = {https://doi.org/10.3390/pharmaceutics18060670},
note = {CC BY},
}
RIS
TY - JOUR
TI - Predicting Blood–Brain Barrier Permeability from Experimental Data: An Interpretable and Externally Validated Machine Learning Framework
AU - Saurabh Tiwari
AU - Katarzyna Mądra-Gackowska
AU - Marcin Gackowski
AU - Nokeun Park
AU - Łukasz Szeleszczuk
PY - 2026
DA - 2026/05/28
JO - Pharmaceutics
DO - 10.3390/pharmaceutics18060670
UR - https://doi.org/10.3390/pharmaceutics18060670
AB - Background: The blood–brain barrier (BBB), which restricts the brain penetration of most small molecules and almost all biologics, continues to be a significant hurdle in the development of drugs for the central nervous system (CNS). During early-stage screening, a reliable computational prediction of BBB permeability, typically expressed as log BB, can help reduce the experimental load. Methods: We provide a well-validated machine learning system created solely using the B3DB experimental database, which includes 7807 chemicals with BBB+/BBB− annotations and 1058 compounds with in vivo log BB values. Using the Mordred library, a carefully selected set of 40 two-dimensional chemical descriptors was calculated from SMILES notation without the use of artificial data augmentation. Stratified five-fold cross-validation was used to comprehensively benchmark the nine methods used in this study. Results: On a held-out test set (n = 212), gradient boosting produced the greatest regression performance, with R2 = 0.6043, RMSE = 0.4740 log units, and MAE = 0.3326, which is in line with the upper range recorded for experimental BBB datasets. On an internal test set (n = 1562), the corresponding classifier obtained an AUC-ROC of 0.9476 and a balanced accuracy of 0.8568; on an independent external validation set (n = 175), it achieved an AUC-ROC of 0.9137. Topological polar surface area was found by SHAP analysis to be the primary factor influencing BBB permeability, with lipophilicity and ionization-related characteristics being the second and third most important factors, respectively. Nonlinear relationships in accordance with accepted pharmacokinetic principles were validated using partial dependence analysis. Conclusion: This study provides a reliable technique for predicting BBB permeability in CNS drug discovery.
SN - 1999-4923
ER -