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Cited by 91Computational Drug Discovery MethodsBarrier Structure and Function StudiesMachine Learning in Healthcare

Improved Classification of Blood-Brain-Barrier Drugs Using Deep Learning

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Miao, Rui, Xia, Liang-Yong, Chen, Hao-Heng, Huang, Hai-Hui, Liang, Yong·2019·Scientific Reports
DOI 10.1038/s41598-019-44773-4PMC PMC6584536
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Abstract

Blood-Brain-Barrier (BBB) is a strict permeability barrier for maintaining the Central Nervous System (CNS) homeostasis. One of the most important conditions to judge a CNS drug is to figure out whether it has BBB permeability or not. In the past 20 years, the existing prediction approaches are usually based on the data of the physical characteristics and chemical structure of drugs. However, these methods are usually only applicable to small molecule compounds based on passive diffusion through BBB. To deal this problem, one of the most famous methods is multi-core SVM method, which is based on clinical phenotypes about Drug Side Effects and Drug Indications to predict drug penetration of BBB. This paper proposed a Deep Learning method to predict the Blood-Brain-Barrier permeability based on the clinical phenotypes data. The validation result on three datasets proved that Deep Learning method achieves better performance than the other existing methods. The average accuracy of our method reaches 0.97, AUC reaches 0.98, and the F1 score is 0.92. The results proved that Deep Learning methods can significantly improve the prediction accuracy of drug BBB permeability and it can help researchers to reduce clinical trials and find new CNS drugs.

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  • Many pharmaceuticals are rendered ineffective against neurological disorders because they fail to penetrate the blood-brain barrier, not because they lack insufficient potency.

    “Many pharmaceuticals are rendered ineffective against neurological disorders because they fail to penetrate the BBB, not because they lack insufficient potency. 47”

    From Therapeutic Properties and Pharmacological Activities of Asiaticoside and Madecassoside: A Review (2023) · cites this paper as [47]

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Cited by

Sorted by citations

Authors
Miao, R., Xia, L.-Y., Chen, H.-H., Huang, H.-H., & Liang, Y.
Affiliations
Macau University of Science and Technology (MO)
Corresponding author
  • Xia, Liang-Yong
  • Liang, Yong
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2019-06-19
Volume
9
Issue
1
Pages
8802-8802
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-019-44773-4
PMC
PMC6584536
OpenAlex
W2949937757
Cited by
91
References
2
Citations by year
2026: 4 · 2025: 19 · 2024: 14 · 2023: 13 · 2022: 11 · 2021: 18 · 2020: 11 · 2019: 1
FWCI
6.97
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
Macau University of Science and Technology Foundation (003/2016/AFJ)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Life Sciences › Neuroscience › Neurology › Barrier Structure and Function Studies
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
blood-brain barrier permeability, BBB permeability prediction, CNS drug discovery, clinical phenotype, adverse drug reactions, drug indications, deep learning
MeSH terms
Deep Learning, Blood-Brain Barrier, Pharmaceutical Preparations, Humans, ROC Curve, Reproducibility of Results, Databases as Topic, Support Vector Machine

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