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BBBper: A Machine Learning-based Online Tool for Blood-brain Barrier (BBB) Permeability Prediction
Pawan Kumar1, Vandana Saini1, Dinesh Gupta2
1Toxicology and Computational Biology Group, Centre for Bioinformatics, Maharshi Dayanand University, Rohtak, 124001, Haryana, India.
CNS & Neurological Disorders Drug Targets
|October 17, 2024
Summary
A new machine learning tool, BBBper, accurately predicts Blood-Brain Barrier (BBB) permeability for drug development. This free online tool achieves 97% accuracy, aiding the creation of new treatments for neurological disorders.
Area of Science:
- Neuroscience
- Pharmacology
- Computational Chemistry
Background:
- Neurological disorders affect over 15% of the global population.
- The Blood-Brain Barrier (BBB) restricts 98% of external compounds, crucial for brain homeostasis.
- Predicting BBB permeability is essential for developing effective drug candidates.
Purpose of the Study:
- To develop an accurate machine learning tool for Blood-Brain Barrier (BBB) permeability prediction.
- To create an accessible online platform for researchers to assess drug-like compounds' BBB penetration.
- To improve the efficiency of drug discovery for neurological disorders.
Main Methods:
- Developed a supervised machine learning model utilizing physicochemical parameters.
- Created a user-end webpage in HTML linked to a Python-based back-end server.
- Implemented a random forest algorithm for prediction analysis.
Main Results:
- The BBBper tool demonstrates 97% accuracy on an external dataset.
- Achieved higher accuracy compared to existing web-based BBB permeability prediction tools (70-92%).
- Successfully developed a functional online tool for predicting BBB permeability.
Conclusions:
- The BBBper web tool offers a highly accurate and freely accessible solution for BBB permeability prediction.
- Facilitates the design and development of novel therapeutics targeting the central nervous system.
- The tool is available at http://bbbper.mdu.ac.in for public use.
