ArticleScientific reports2019
Improved Classification of Blood-Brain-Barrier Drugs Using Deep Learning.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed.
- Feature-Guided Machine Learning for Studying Passive Blood-Brain Barrier Permeability to Aid Drug Discovery.International journal of molecular sciences · 2025Article
- 3BTRON: A Blood-Brain Barrier Recognition Network.Communications biology · 2025Article
- Applications of artificial intelligence in drug discovery for neurological diseases.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025Review
- A Classification-Based Blood-Brain Barrier Model: A Comparative Approach.Pharmaceuticals (Basel, Switzerland) · 2025Article
- GCN-BBB: Deep Learning Blood-Brain Barrier (BBB) Permeability PharmacoAnalytics with Graph Convolutional Neural (GCN) Network.The AAPS journal · 2025Article
- Enhancing Blood-Brain Barrier Penetration Prediction by Machine Learning-Based Integration of Novel and Existing, In Silico and Experimental Molecular Parameters from a Standardized Database.Journal of chemical information and modeling · 2025Article
- In vitro and in silico evaluation of synthetic compounds derived from bi-triazoles against asexual and sexual forms of Plasmodium falciparum.Malaria journal · 2025Article
- Machine Learning in Drug Development for Neurological Diseases: A Review of Blood Brain Barrier Permeability Prediction Models.Molecular informatics · 2025Review
- Computational Modeling of Pharmaceuticals with an Emphasis on Crossing the Blood-Brain Barrier.Pharmaceuticals (Basel, Switzerland) · 2025Review
- A review on the role of nanoparticles for targeted brain drug delivery: synthesis, characterization, and applications.EXCLI journal · 2025Review
- SerotoninAI: Serotonergic System Focused, Artificial Intelligence-Based Application for Drug Discovery.Journal of chemical information and modeling · 2024Article
- Experimental and Computational Methods to Assess Central Nervous System Penetration of Small Molecules.Molecules (Basel, Switzerland) · 2024Review
- Article
- Brain Tumors: Development, Drug Resistance, and Sensitization - An Epigenetic Approach.Epigenetics · 2023Review
- De novo design of bioactive phenol and chromone derivatives for inhibitors of Spike glycoprotein of SARS-CoV-2 in silico.3 Biotech · 2023Article
- Therapeutic properties and pharmacological activities of asiaticoside and madecassoside: A review.Journal of cellular and molecular medicine · 2023Review
- Computer-Aided Drug Design towards New Psychotropic and Neurological Drugs.Molecules (Basel, Switzerland) · 2023Review
- Recent Studies of Artificial Intelligence on In Silico Drug Distribution Prediction.International journal of molecular sciences · 2023Review
- Identification of 1,2,3-triazole-phthalimide derivatives as potential drugs against COVID-19: a virtual screening, docking and molecular dynamic study.Journal of biomolecular structure & dynamics · 2022Article
- Impairing proliferation of glioblastoma multiforme with CD44+ selective conjugated polymer nanoparticles.Scientific reports · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.