Resampling combined with stacking learning for prediction of blood-brain barrier permeability of compounds_Journal of Biomedical Engineering

Authors：

SU Qing ¹ , XIAO Ganyao ¹ ,  ZHOU Wei ² , DU Zhiyun ²

1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, P. R. China;
2. School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou 510006, P. R. China;

Corresponding author：

ZHOU Wei, Email: zhou_wei@gdut.edu.cn

Keywords：

Blood-brain barrier permeability; Machine learning; Blood-brain barrier positive quantification intervals; Resampling algorithms; Stacking learning algorithm

DOI：

10.7507/1001-5515.202210067

Video：

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Abstract Full text Figures/Tables Video References Cited by

It is a significant challenge to improve the blood-brain barrier (BBB) permeability of central nervous system (CNS) drugs in their development. Compared with traditional pharmacokinetic property tests, machine learning techniques have been proven to effectively and cost-effectively predict the BBB permeability of CNS drugs. In this study, we introduce a high-performance BBB permeability prediction model named balanced-stacking-learning based BBB permeability predictor(BSL-B3PP). Firstly, we screen out the feature set that has a strong influence on BBB permeability from the perspective of medicinal chemistry background and machine learning respectively, and summarize the BBB positive(BBB+) quantification intervals. Then, a combination of resampling algorithms and stacking learning(SL) algorithm is used for predicting the BBB permeability of CNS drugs. The BSL-B3PP model is constructed based on a large-scale BBB database (B3DB). Experimental validation shows an area under curve (AUC) of 97.8% and a Matthews correlation coefficient (MCC) of 85.5%. This model demonstrates promising BBB permeability prediction capability, particularly for drugs that cannot penetrate the BBB, which helps reduce CNS drug development costs and accelerate the CNS drug development process.

Citation： SU Qing, XIAO Ganyao, ZHOU Wei, DU Zhiyun. Resampling combined with stacking learning for prediction of blood-brain barrier permeability of compounds. Journal of Biomedical Engineering, 2023, 40(4): 753-761. doi: 10.7507/1001-5515.202210067 Copy

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2.	Liu L, Zhang L, Feng H, et al. Prediction of the blood–brain barrier (BBB) permeability of chemicals based on machine-learning and ensemble methods. Chemical Research in Toxicology, 2021, 34(6): 1456-1467.
3.	Wang Z, Yang H, Wu Z, et al. In silico prediction of blood–brain barrier permeability of compounds by machine learning and resampling methods. ChemMedChem, 2018, 13(20): 2189-2201.
4.	Plisson F, Piggott A M. Predicting blood–brain barrier permeability of marine-derived kinase inhibitors using ensemble classifiers reveals potential hits for neurodegenerative disorders. Marine drugs, 2019, 17(2): 81.
5.	Reddy G T, Bhattacharya S, Ramakrishnan S S, et al. An ensemble based machine learning model for diabetic retinopathy classification//2020 International Conference on Emerging Trends in Information Technology and Engineering (IC-ETITE). IEEE, 2020.
6.	Wolpert D H. Stacked generalization. Neural networks, 1992, 5(2): 241-259.
7.	Pernía-Espinoza A, Fernández-Ceniceros J, Antonanzas J, et al. Stacking ensemble with parsimonious base models to improve generalization capability in the characterization of steel bolted components. Applied Soft Computing, 2018, 70: 737-750.
8.	Taspinar Y S, Cinar I, Koklu M. Classification by a stacking model using CNN features for COVID-19 infection diagnosis. Journal of X-ray Science and Technology, 2022, 30(1): 73-88.
9.	Bustamam A, Yanuar A, Anki P, et al. Evaluation quantitative structure-activity relationship (QSAR) using ensemble learning methods on acetylcholinesterase inhibitors for Alzheimer's disease. Commun Math Biol Neurosci, 2022, 2022: 73.
10.	Meng F, Xi Y, Huang J, et al. A curated diverse molecular database of blood-brain barrier permeability with chemical descriptors. Scientific Data, 2021, 8(1): 289.
11.	Moriwaki H, Tian Y S, Kawashita N, et al. Mordred: a molecular descriptor calculator. Journal of cheminformatics, 2018, 10(1): 4.
12.	O'Boyle N M, Banck M, James C A, et al. Open babel: an open chemical toolbox. Journal of Cheminformatics, 2011, 3: 33.
13.	Lipinski C A, Lombardo F, Dominy B W, et al. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 2001, 46(1-3): 3-26.
14.	Lipinski C A. Lead-and drug-like compounds: the rule-of-five revolution. Drug Discovery Today: Technologies, 2004, 1(4): 337-341.
15.	Pajouhesh H, Lenz G R. Medicinal chemical properties of successful central nervous system drugs. NeuroRx, 2005, 2(4): 541-553.
16.	Xiong B, Wang Y, Chen Y, et al. Strategies for structural modification of small molecules to improve blood–brain barrier penetration: a recent perspective. Journal of Medicinal Chemistry, 2021, 64(18): 13152-13173.
17.	Mahar Doan K M, Humphreys J E, Webster L O, et al. Passive permeability and P-glycoprotein-mediated efflux differentiate central nervous system (CNS) and non-CNS marketed drugs. Journal of Pharmacology and Experimental Therapeutics, 2002, 303(3): 1029-1037.
18.	Gupta M, Lee H J, Barden C J, et al. The blood–brain barrier (BBB) score. Journal of Medicinal Chemistry, 2019, 62(21): 9824-9836.
19.	Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in Python. The Journal of Machine Learning Research, 2011, 12: 2825-2830.
20.	Lemaître G, Nogueira F, Aridas C K. Imbalanced-learn: a Python toolbox to tackle the curse of imbalanced datasets in machine learning. The Journal of Machine Learning Research, 2017, 18(1): 559-563.
21.	宋智文. 基于集成学习的短文本分类方法. 重庆: 重庆邮电大学, 2020.

1. Kumar R, Sharma A, Tiwari R K. Can we predict blood brain barrier permeability of ligands using computational approaches?. Interdisciplinary Sciences: Computational Life Sciences, 2013, 5(2): 95-101.
2. Liu L, Zhang L, Feng H, et al. Prediction of the blood–brain barrier (BBB) permeability of chemicals based on machine-learning and ensemble methods. Chemical Research in Toxicology, 2021, 34(6): 1456-1467.
3. Wang Z, Yang H, Wu Z, et al. In silico prediction of blood–brain barrier permeability of compounds by machine learning and resampling methods. ChemMedChem, 2018, 13(20): 2189-2201.
4. Plisson F, Piggott A M. Predicting blood–brain barrier permeability of marine-derived kinase inhibitors using ensemble classifiers reveals potential hits for neurodegenerative disorders. Marine drugs, 2019, 17(2): 81.
5. Reddy G T, Bhattacharya S, Ramakrishnan S S, et al. An ensemble based machine learning model for diabetic retinopathy classification//2020 International Conference on Emerging Trends in Information Technology and Engineering (IC-ETITE). IEEE, 2020.
6. Wolpert D H. Stacked generalization. Neural networks, 1992, 5(2): 241-259.
7. Pernía-Espinoza A, Fernández-Ceniceros J, Antonanzas J, et al. Stacking ensemble with parsimonious base models to improve generalization capability in the characterization of steel bolted components. Applied Soft Computing, 2018, 70: 737-750.
8. Taspinar Y S, Cinar I, Koklu M. Classification by a stacking model using CNN features for COVID-19 infection diagnosis. Journal of X-ray Science and Technology, 2022, 30(1): 73-88.
9. Bustamam A, Yanuar A, Anki P, et al. Evaluation quantitative structure-activity relationship (QSAR) using ensemble learning methods on acetylcholinesterase inhibitors for Alzheimer's disease. Commun Math Biol Neurosci, 2022, 2022: 73.
10. Meng F, Xi Y, Huang J, et al. A curated diverse molecular database of blood-brain barrier permeability with chemical descriptors. Scientific Data, 2021, 8(1): 289.
11. Moriwaki H, Tian Y S, Kawashita N, et al. Mordred: a molecular descriptor calculator. Journal of cheminformatics, 2018, 10(1): 4.
12. O'Boyle N M, Banck M, James C A, et al. Open babel: an open chemical toolbox. Journal of Cheminformatics, 2011, 3: 33.
13. Lipinski C A, Lombardo F, Dominy B W, et al. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 2001, 46(1-3): 3-26.
14. Lipinski C A. Lead-and drug-like compounds: the rule-of-five revolution. Drug Discovery Today: Technologies, 2004, 1(4): 337-341.
15. Pajouhesh H, Lenz G R. Medicinal chemical properties of successful central nervous system drugs. NeuroRx, 2005, 2(4): 541-553.
16. Xiong B, Wang Y, Chen Y, et al. Strategies for structural modification of small molecules to improve blood–brain barrier penetration: a recent perspective. Journal of Medicinal Chemistry, 2021, 64(18): 13152-13173.
17. Mahar Doan K M, Humphreys J E, Webster L O, et al. Passive permeability and P-glycoprotein-mediated efflux differentiate central nervous system (CNS) and non-CNS marketed drugs. Journal of Pharmacology and Experimental Therapeutics, 2002, 303(3): 1029-1037.
18. Gupta M, Lee H J, Barden C J, et al. The blood–brain barrier (BBB) score. Journal of Medicinal Chemistry, 2019, 62(21): 9824-9836.
19. Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in Python. The Journal of Machine Learning Research, 2011, 12: 2825-2830.
20. Lemaître G, Nogueira F, Aridas C K. Imbalanced-learn: a Python toolbox to tackle the curse of imbalanced datasets in machine learning. The Journal of Machine Learning Research, 2017, 18(1): 559-563.
21. 宋智文. 基于集成学习的短文本分类方法. 重庆: 重庆邮电大学, 2020.

Journal of Biomedical Engineering

Resampling combined with stacking learning for prediction of blood-brain barrier permeability of compounds

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