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image of BBBper: A Machine Learning-based Online Tool for Blood-Brain Barrier (BBB) Permeability Prediction

Abstract

Aims

Neuronal 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).

Background

BBB 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.

Objective

Here, we report about developing BBBper (Blood-Brain Barrier permeability prediction) using machine learning tool.

Method

A 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.

Result

BBBper 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.

Conclusion

The BBBper web tool is freely available at http://bbbper.mdu.ac.in.

© 2024 Bentham Science Publishers
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/content/journals/cnsnddt/10.2174/0118715273328174241007060331
2024-10-16
2025-03-07

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/content/journals/cnsnddt/10.2174/0118715273328174241007060331
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BBBper: A Machine Learning-based Online Tool for Blood-Brain Barrier (BBB) Permeability Prediction
Bentham Science Publishers ; https://doi.org/10.2174/0118715273328174241007060331
/content/journals/cnsnddt/10.2174/0118715273328174241007060331
/content/journals/cnsnddt/10.2174/0118715273328174241007060331
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  • Article Type:     Research Article
Keywords: random forest ; Python ; BBB permeability ; Web-server tool ; supervised machine learning ; Blood-brain permeation

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