血脑屏障
磁导率
计算机科学
机器学习
药物发现
人工智能
化学
神经科学
生物信息学
生物
中枢神经系统
膜
生物化学
作者
Deeksha Saxena,Anju Sharma,Mohammed Haris Siddiqui,Kumar Rajnish
出处
期刊:Current Pharmaceutical Biotechnology
[Bentham Science]
日期:2019-11-15
卷期号:20 (14): 1163-1171
被引量:30
标识
DOI:10.2174/1389201020666190821145346
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI