ArticleCNS & neurological disorders drug targets2026
BBBper: A Machine Learning-based Online Tool for Blood-brain Barrier (BBB) Permeability Prediction.
Article in CNS & neurological disorders drug targets, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Kinsenoside Targets IDH1 to Restore Microglial Immune-Metabolic Homeostasis for Alzheimer's Disease Therapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Artificial intelligence revolutionizing CNS drug discovery and development.Drug discovery today · 2026Review
- Trends and Perspectives in the Rational Management of CNS Disorders.CNS & neurological disorders drug targets · 2026Article
- Animal Venoms as Peptide Libraries for the Discovery of Antiglioblastoma Agents.Biochemistry research international · 2026Review
- Computational drug repositioning approach to predict multi-target therapeutics for epilepsy.Scientific reports · 2025Article
- Prodrug Approach as a Strategy to Enhance Drug Permeability.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Neurotherapeutics across blood-brain barrier: screening of BBB-permeable and CNS-active molecules for neurodegenerative disease.Frontiers in pharmacology · 2025Article
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Authors and funding
5 authors.
Funding
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Abstract
introductionBBB limits the permeability of external compounds by 98% to maintain and regulate brain homeostasis. Hence, BBB permeability prediction is vital to predict the activity of a drug-like substance.
aimNeuronal disorders have affected more than 15% of the world's population, signifying the importance of continued design and development of drugs that can cross the Blood-Brain Barrier (BBB).
objectiveHere, we report about developing BBBper (Blood-Brain Barrier permeability prediction) using machine learning tool.
methodsA supervised machine learning-based online tool, based on physicochemical parameters to predict the BBB permeability of given chemical compounds was developed. The user-end webpage was developed in HTML and linked with back-end server by a python script to run user queries and results.
resultsBBBper uses a random forest algorithm at the back end, showing 97% accuracy on the external dataset, compared to 70-92% accuracy of currently available web-based BBB permeability prediction tools.
conclusionThe BBBper web tool is freely available at http://bbbper.mdu.ac.in.
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