Blood Brain Barrier Permeability Prediction Using Machine Learning Techniques: An Update

Deeksha       Saxena; Anju       Sharma; Mohammed     H.   Siddiqui; Rajnish       Kumar
doi:10.2174/1389201020666190821145346
Abstract

Blood Brain Barrier (BBB) is the collection of vessels of blood with special properties of permeability that allow a limited range of drug and compounds to pass through it. The BBB plays a vital role in maintaining balance between intracellular and extracellular environment for brain. Brain Capillary Endothelial Cells (BECs) act as vehicle for transport and the transport mechanisms across BBB involve active and passive diffusion of compounds. Efficient prediction models of BBB permeability can be vital at the preliminary stages of drug development. There have been persistent efforts in identifying the prediction of BBB permeability of compounds employing multiple machine learning methods in an attempt to minimize the attrition rate of drug candidates taking up preclinical and clinical trials. However, there is an urgent need to review the progress of such machine learning derived prediction models in the prediction of BBB permeability. In the current article, we have analyzed the recently developed prediction model for BBB permeability using machine learning.
Keywords: Blood brain barrier, machine learning, model, permeability, prediction, central nervous system.
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DOI https://dx.doi.org/10.2174/1389201020666190821145346	Print ISSN 1389-2010
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Current Pharmaceutical Biotechnology

Blood Brain Barrier Permeability Prediction Using Machine Learning Techniques: An Update

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