Prediction of blood-brain barrier permeability of compounds by machine learning algorithms

Feng, Tan wei and Raihana Zahirah, Edros and Ngahzaifa, Ab Ghani and Siti Umairah, Mokhtar and Dong, Ruihai (2024) Prediction of blood-brain barrier permeability of compounds by machine learning algorithms. Journal of Advanced Research in Applied Sciences and Engineering Technology, 33 (2). pp. 269-276. ISSN 2462-1943. (Published)

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In the drug development for the Central Nervous System (CNS), the discovery of the compounds that can pass through the brain across the Blood-Brain Barrier (BBB) is the most challenging assessment. Almost 98% of small molecules are unable to permeate BBB, reducing the pharmacokinetics of the drugs in the CNS by affecting its absorption, distribution, metabolism, and excretion (ADME) mechanisms. Since the CNS is often inaccessible to many complex procedures and performing in-vitro permeability studies for thousands of compounds can be laborious, attempts were made to predict the permeation of compounds through BBB by implementing the Machine Learning (ML) approach. In this work, using the KNIME Analytics platform, 4 predictive models were developed with 4 ML algorithms followed by a ten-fold cross-validation approach to predict the external validation set. Among 4 ML algorithms, Extreme Gradient Boosting (XGBoost) overperformed in BBB permeability prediction and was chosen as the prediction model for deployment. Data pre-processing and feature selection enhanced the prediction of the model. Overall, the model achieved 86.7% and 88.5% of accuracy and 0.843 and 0.927 AUC, respectively in the training set and external validation set, proving that the model with high stability in prediction.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Blood brain barrier; classification; Machine learning
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TP Chemical technology
Faculty/Division: Faculty of Industrial Sciences And Technology
Faculty of Chemical and Process Engineering Technology
Faculty of Computing
Depositing User: Mr Muhamad Firdaus Janih@Jaini
Date Deposited: 12 Jan 2024 03:36
Last Modified: 12 Jan 2024 03:36
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