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基于机器学习技术的血脑屏障通透性预测:更新。

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

机构信息

Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow-226028, Uttar Pradesh, India.

Department of Applied Science, Indian Institute of Information Technology, Allahabad, Uttar Pradesh, India.

出版信息

Curr Pharm Biotechnol. 2019;20(14):1163-1171. 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.

摘要

血脑屏障(BBB)是具有特殊通透性的血管集合,允许有限范围的药物和化合物通过。BBB 在维持大脑细胞内外环境平衡方面起着至关重要的作用。脑毛细血管内皮细胞(BEC)是运输的载体,而 BBB 中的运输机制涉及化合物的主动和被动扩散。在药物开发的初步阶段,能够有效预测 BBB 通透性的模型至关重要。人们一直致力于采用多种机器学习方法来识别化合物的 BBB 通透性预测,以尽量减少进入临床前和临床试验的候选药物的淘汰率。然而,迫切需要审查基于机器学习的此类预测模型在 BBB 通透性预测方面的进展。在本文中,我们分析了最近使用机器学习开发的 BBB 通透性预测模型。

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