Prediction of blood-brain barrier permeability of compounds by machine learning algorithms
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 i...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Semarak Ilmu Publishing
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/39985/ http://umpir.ump.edu.my/id/eprint/39985/1/Prediction%20of%20blood-brain%20barrier%20permeability%20of%20compounds%20by%20machine.pdf |
| _version_ | 1848825919798509568 |
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| author | Feng, Tan wei Raihana Zahirah, Edros Ngahzaifa, Ab Ghani Siti Umairah, Mokhtar Dong, Ruihai |
| author_facet | Feng, Tan wei Raihana Zahirah, Edros Ngahzaifa, Ab Ghani Siti Umairah, Mokhtar Dong, Ruihai |
| author_sort | Feng, Tan wei |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:36:35Z |
| format | Article |
| id | ump-39985 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:36:35Z |
| publishDate | 2024 |
| publisher | Semarak Ilmu Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-399852024-01-12T03:36:23Z http://umpir.ump.edu.my/id/eprint/39985/ Prediction of blood-brain barrier permeability of compounds by machine learning algorithms Feng, Tan wei Raihana Zahirah, Edros Ngahzaifa, Ab Ghani Siti Umairah, Mokhtar Dong, Ruihai HD28 Management. Industrial Management QA75 Electronic computers. Computer science T Technology (General) TA Engineering (General). Civil engineering (General) TP Chemical technology 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. Semarak Ilmu Publishing 2024-01 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/39985/1/Prediction%20of%20blood-brain%20barrier%20permeability%20of%20compounds%20by%20machine.pdf 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) https://doi.org/10.37934/araset.33.2.269276 https://doi.org/10.37934/araset.33.2.269276 |
| spellingShingle | HD28 Management. Industrial Management QA75 Electronic computers. Computer science T Technology (General) TA Engineering (General). Civil engineering (General) TP Chemical technology Feng, Tan wei Raihana Zahirah, Edros Ngahzaifa, Ab Ghani Siti Umairah, Mokhtar Dong, Ruihai Prediction of blood-brain barrier permeability of compounds by machine learning algorithms |
| title | Prediction of blood-brain barrier permeability of compounds by machine learning algorithms |
| title_full | Prediction of blood-brain barrier permeability of compounds by machine learning algorithms |
| title_fullStr | Prediction of blood-brain barrier permeability of compounds by machine learning algorithms |
| title_full_unstemmed | Prediction of blood-brain barrier permeability of compounds by machine learning algorithms |
| title_short | Prediction of blood-brain barrier permeability of compounds by machine learning algorithms |
| title_sort | prediction of blood-brain barrier permeability of compounds by machine learning algorithms |
| topic | HD28 Management. Industrial Management QA75 Electronic computers. Computer science T Technology (General) TA Engineering (General). Civil engineering (General) TP Chemical technology |
| url | http://umpir.ump.edu.my/id/eprint/39985/ http://umpir.ump.edu.my/id/eprint/39985/ http://umpir.ump.edu.my/id/eprint/39985/ http://umpir.ump.edu.my/id/eprint/39985/1/Prediction%20of%20blood-brain%20barrier%20permeability%20of%20compounds%20by%20machine.pdf |